Predicting outcome in colorectal cancer subjects

Abstract

The present invention relates to a method of predicting an outcome in a subject with colorectal cancer, the method comprising the steps of determining or receiving a result of determining a first gene expression profile for each of one or more immune defense response genes, and / or a second gene expression profile for each of one or more T cell receptor signaling genes, and / or a third gene expression profile for each of one or more PDE4D7 related genes, wherein said first, second and third expression profiles are determined in a biological sample obtained from said colorectal cancer subject; determining a prediction of the outcome of said colorectal cancer subject based on said first gene expression profile, or said second gene expression profile, or said third gene expression profile, or said first, second and third gene expression profiles; and optionally providing the prediction of the outcome of the colorectal cancer subject to a healthcare caregiver or to said colorectal cancer subject.

Description

[Technical field]

[0001] The present invention relates to a method for predicting outcome in a subject with colorectal cancer and an apparatus for predicting outcome in a subject with colorectal cancer.Furthermore, the present invention relates to a diagnostic kit, the use of the kit, the use of the kit in a method for predicting outcome in a subject with colorectal cancer, the use of the first, second and / or third gene expression profile in a method for predicting outcome in a subject with colorectal cancer, and corresponding computer programs. [Background technology]

[0002] Cancer is a class of diseases in which a group of cells exhibit uncontrolled proliferation, invasion, and sometimes metastasis. These three malignant properties of cancer distinguish it from benign tumors, which are self-limited and do not invade or metastasize. Colorectal cancer (CRC) is an extremely common cancer, ranking fourth in new cases (8.2%) and second in estimated deaths in the United States in 2020. It is estimated that nearly 148,000 new cases of colorectal cancer will be diagnosed in 2020, and more than 53,000 people will die from the disease. The median age at diagnosis is 67 years, with a relative 5-year survival rate of 64.6%. The earlier colorectal cancer is diagnosed, the higher the chance of surviving more than 5 years after diagnosis; while the 5-year survival rate for localized colorectal cancer is 90.2%, only 14.3% of patients with distant colorectal cancer survive at least 5 years. Approximately 38% of patients present with localized disease. Men are affected more than women, and incidence increases with age (see National Cancer Institute, SEER Cancer Stat Facts: Colorectal Cancer, accessed September 9, 2020, https: / / seer.cancer.gov / statfacts / html / colorect.html). Risk factors for colorectal cancer include increasing age, a family history of colorectal cancer in first-degree relatives, a history of colon, rectal, or ovarian cancer, a history of high-risk adenomas, genetic changes in certain genes that increase the risk of familial adenomatous polyposis (FAP) or Lynch syndrome, a history of chronic ulcerative colitis or Crohn's disease for more than 8 years, drinking 3 or more drinks per day, smoking, black race, and obesity. Over the past 20 years, the rate of new cases has decreased, although more rapidly than the decline in mortality.

[0003] Colorectal cancer may also start in the colon or rectum and be called colon cancer or rectal cancer depending on where it begins. Nevertheless, colon cancer and rectal cancer are often grouped together because they have many common characteristics. CRC usually starts as growths on the lining of the colon or rectum, called polyps. Whether these polyps turn into cancer depends on the type of polyp. Adenomas can turn into cancer. Hyperplastic and inflammatory polyps are more common, but are generally not precancerous. Sessile serrated polyps (SSPs) and traditional serrated adenomas (TSAs) are often treated as adenomas because they are at higher risk for colorectal cancer. In addition to the type of polyp, larger size of polyps, multiple polyps, and dysplasia present in polyps increase the risk of developing CRC.

[0004] Most CRCs are adenocarcinomas, which begin in the cells that produce mucus to lubricate the lining of the colon and rectum. Other types, less commonly, are carcinoid tumors (which begin in hormone-producing cells), gastrointestinal stromal tumors (GISTs), lymphomas, and sarcomas.

[0005] Local treatments are more useful in early cancer. Systemic treatments are also performed depending on the type of CRC. For each type and stage, different treatment types are used, or used together at the same time, or after each other. Some major treatment types are described in American Cancer Society, Colorectal Cancer, https: / / www.cancer.org / cancer / colon-rectal-cancer.html, accessed September 9, 2020, and National Cancer Institute, PDQ® Adult Treatment Editorial Board, PDQ Colorectal Cancer Treatment, https: / / www.cancer.gov / types / colorectal / patient / colon-treatment-pdq, accessed September 9, 2020.

[0006] The main treatment for early stage colorectal cancer is surgery. The type of surgery depends on the grade of the cancer. In the case of rectal cancer, radiation and chemotherapy are often performed before and after surgery. Small metastases can be removed by embolization or ablation. This is also an option for patients whose cancer cannot be cured or operated on. Radiation therapy is not common in colon cancer treatment, but is used in special cases, such as in the (neo)adjuvant setting in combination with surgery or to treat metastases. In the case of rectal cancer, radiation therapy is much more common and can be used pre-operatively, post-operatively or intraoperatively. Also, in cases where surgery is not possible, radiation therapy can be used with or without chemotherapy. Chemotherapy is used at different times during the treatment of colorectal cancer, in the (neo)adjuvant setting or to treat metastases. Chemotherapy can be given systemically or locally. Targeted therapies used in colorectal cancer can be targeted: VEGF to prevent angiogenesis, EGFR and BRAF to prevent cancer growth. Kinase inhibitors can also be used to slow the growth of cancer cells. Immune checkpoint inhibitors (PD-1 inhibitors, CTLA4-inhibitors) can be used for people whose colorectal cancer cells test positive for certain genetic alterations, such as microsatellite instability (MSI-H) or mismatch repair (MMR) genes. These drugs are often used when surgery is not possible or for recurrent or metastatic CRC.

[0007] Traditional classification of tumors has been refined by comprehensive molecular characterization using large numbers of tumors. Colorectal cancers can be subgrouped by chromosomal instability (CIN), microsatellite instability (MSI) and hypermethylation. The Cancer Genome Atlas Network published a comprehensive molecular characterization of CRC in 2012 (see Cancer Genome Atlas Network, "Comprehensive molecular characterization of human colon and rectal cancer," Nature, Vol. 487, No. 7407, pp. 330-337, 2012). Numerous studies have previously reported on genes and pathways important for the initiation and progression of CRC, such as WNT, RAS-MAPK, PI3K, TGF-β, P53, and DNA mismatch repair pathways. The TCGA study concluded that non-hypermutated adenocarcinomas of the colon and rectum cannot be distinguished at the genomic level. It is almost universal in CRC that activation of the WNT signaling pathway and inactivation of the TGF-β signaling pathway result in reduced activity of MYC. Genomic abnormalities frequently target the PI3K and MAPK pathways and less frequently receptor tyrosine kinases. Later, Liu Y. et al. made a systematic attempt to characterize the molecular processes shared by gastrointestinal cancers (including CRC) (Liu Y. et al., "Comparative molecular analysis of gastrointestinal adenocarcinomas," Cancer Cell, Vol. 33, No. 4, pp. 721-735, 2018). They found enhanced activation of WNT signaling, especially in the lower gastrointestinal tract. They also found disruption of TGF-β and SMAD signaling components, consistent with previous findings. It becomes essential to consider molecular subtypes to study and treat CRC. For more detailed details, see Liu Y. et al., 2018, ibid. and Cancer Genome Atlas Network, 2018, ibid.

[0008] For example, European Patent Application Publication No. 1777523A1, WO2007 / 082099A2, He et al., 2020 (Journal of Cancer, col. Vol. 11, No. 4, January 1, 2020, pp. 893-905), He et al., 2019 (Translatinal Cancer Research, Vol. 8, No. 4, August 1, 2019, pp. 1351-1363), Sheelu et al., (Annals of Surgical Oncology, Vol. 14, No. 12, September 25, 2007, pp. 3460-3471) and WO2016 / 109546A2 describe marker genes for colorectal cancer.

[0009] Prediction of treatment outcome is very complicated because many factors are involved in treatment efficacy and disease recurrence. Important factors may not yet be identified, and the influence of other factors cannot be precisely determined. Several clinicopathological measurements are currently being investigated and applied in clinical practice to improve response prediction and treatment selection, and have brought about some improvements. Nevertheless, better prediction of treatment response is still strongly needed to increase the success rate of these treatments. Summary of the Invention [Problem to be solved by the invention]

[0010] It is an object of the present invention to provide a method for predicting outcome in a subject with colorectal cancer, which allows for making better treatment decisions, and an apparatus for predicting outcome in a subject with colorectal cancer. It is a further aspect of the present invention to provide a diagnostic kit, use of the kit, use of the kit in a method for predicting outcome in a subject with colorectal cancer, use of the first, second and / or third gene expression profile in a method for predicting outcome in a subject with colorectal cancer, and corresponding computer programs. [Means for solving the problem]

[0011] In a first aspect of the present invention there is provided a method of predicting outcome in a subject with colorectal cancer, said method comprising: determining or receiving results of the determination of expression levels of six or more genes selected from a first gene expression profile, a second gene expression profile, and a third gene expression profile, wherein the six or more gene expression levels include at least one or more gene expression levels selected from the third gene expression profile and at least one or more gene expression levels selected from the first and / or second gene expression profiles; the first gene expression profile consists of immune defense response genes selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; the second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; the third gene expression profile consists of PDE4D7-related genes selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2; the first, second and third gene expression profiles are determined in a biological sample obtained from the subject with colorectal cancer; determining a prognosis of outcome for the colorectal cancer subject based on expression levels of six or more genes; Optionally, providing a prediction of outcome for said colorectal cancer subject to a health care provider or to said colorectal cancer subject; In one embodiment, the six or more gene expression levels include one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the immune defense response genes, and one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the T cell receptor signaling genes, and one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the PDE4D7-related genes.

[0012] Alternatively, the present invention provides a method for predicting outcome in a subject with colorectal cancer, said method comprising: determining or receiving the results of determining a first gene expression profile for each of one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all, immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, wherein the first gene expression profile is determined in a biological sample obtained from the colorectal cancer subject; and / or determining or receiving the results of a determination of a second gene expression profile for each of one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all, T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70, wherein the second gene expression profile is determined in a biological sample obtained from the colorectal cancer subject; and / or determining or receiving the results of determining a third gene expression profile for each of one or more, e.g., one, two, three, four, five, six, seven or all, PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2, wherein the third gene expression profile is determined in a biological sample obtained from the colorectal cancer subject; determining a prediction of outcome for the colorectal cancer subject based on the first gene expression profile, or the second gene expression profile, or the third gene expression profile, or the first, second and third gene expression profiles; Optionally, providing a prediction of outcome for said colorectal cancer subject to a health care provider or to said colorectal cancer subject. The present invention relates to a method having the following structure:

[0013] In recent years, the importance of the immune system in cancer suppression, as well as in cancer initiation, promotion and metastasis, has become very evident (see Mantovani A. et al., "Cancer-related inflammation", Nature, Vol. 454, No. 7203, pp. 436-444, 2008 and Giraldo NA et al., "The clinical role of the TME in solid cancer", 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 interact and shape each other. Thus, while antitumor immunity can prevent tumor formation, an inflammatory tumor environment promotes cancer initiation and growth. At the same time, tumor cells that arise in an immune system-independent manner can shape the immune microenvironment by recruiting immune cells and have pro-inflammatory effects while also suppressing antitumor immunity.

[0014] Some immune cells in the tumor microenvironment have either general tumor-promoting or general tumor-suppressing effects, whereas other immune cells exhibit plasticity and show both tumor-promoting and tumor-suppressing potential. Thus, the overall immune microenvironment of a tumor is a mix of the various 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., 2019, supra).

[0015] The above principles regarding the role of the immune system in cancer in general also apply to prostate cancer. Chronic inflammation has been linked to the formation of benign as well as malignant prostate tissue (see Hall WA et al., 2016, ibid.), and most prostate cancer tissue samples show immune cell infiltrates. The presence of certain immune cells with tumor-promoting effects has been correlated with poor prognosis, while tumors with more activated natural killer cells have shown better responses to treatment and longer recurrence-free periods (see Shiao SL et al., "Regulation of prostate cancer progression by tumor microenvironment," Cancer Lett, Vol. 380, No. 1, pp. 340-348, 2016).

[0016] Treatment is influenced by immune components of the tumor microenvironment, but RT itself also has extensive effects on 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). Because suppressive cell types are relatively radioinsensitive, their relative numbers increase. Counterproductively, a given radiation insult activates cell survival pathways and stimulates the immune system, which induces an inflammatory response and the recruitment of immune cells. Whether the net effect is tumor-promoting or tumor-suppressing is currently unclear, but its potential for enhancing cancer immunotherapy is being explored.

[0017] The present invention is based on the idea that since the status of the immune system and the immune microenvironment influences treatment efficacy, the ability to identify markers predictive of this influence will help enable better prediction of outcome in colorectal cancer subjects.

[0018] As used herein, the term "outcome" refers to a particular result or effect that can be measured. Examples of outcomes of colorectal cancer subjects include colorectal cancer outcomes, pathology outcomes, outcomes of treatments for colorectal cancer, such as surgery outcomes, radiotherapy outcomes, chemotherapy outcomes, and immunotherapy outcomes, as well as other outcomes such as biomarker-related outcomes (e.g., CEA (carcinoembryonic antigen)), genomic profile-related outcomes, imaging-related outcomes (e.g., changes in tumor morphology or texture), biology-related outcomes (e.g., inflammation or immune response), surrogate marker-related outcomes, tumor size outcomes, treatment side effects outcomes, treatment toxicity outcomes, disease pain outcomes, quality of life outcomes, cancer-specific and overall survival, etc.

[0019] Immune response defense genes Genomic DNA integrity and stability are permanently under oxidative and replicative stress, as well as stress induced by various internal and external cell factors, such as exposure to radiation, viral or bacterial infections (see Gasser S. et al., "Sensing of dangerous DNA", Mechanisms of Aging and Development, Vol. 165, pp. 33-46, 2017). To maintain DNA structure and stability, cells must be able to recognize all types of DNA damage, such as single- or double-strand breaks, induced by various factors. This process involves a number of specific proteins depending on the type of damage as part of the DNA recognition pathway.

[0020] Recent evidence suggests that mislocalized DNA (e.g., DNA that aberrantly appears in the cytoplasmic compartment of a cell rather than in the nucleus) and damaged DNA (e.g., due to mutations occurring in cancer development) are used by the immune system to identify infected and otherwise diseased cells, whereas genomic and mitochondrial DNA present in healthy cells is ignored by the DNA recognition pathway. In diseased cells, cytoplasmic DNA sensor proteins have been demonstrated to be involved in the detection of non-naturally occurring DNA in the cytosol of cells. Detection of such DNA by different nucleic acid sensors results in a similar response that leads to nuclear factor kappa B (NF-kB) and type I interferon (type I IFN) signaling and subsequent activation of innate immune system components. Although it is known that recognition of viral DNA induces type I IFN responses, evidence that sensing of DNA damage initiates an immune response has only recently accumulated.

[0021] TLR9 (Toll-like receptor 9), located in endosomes, was one of the first DNA sensor molecules identified to be involved in immune recognition of DNA by signaling downstream through the adaptor protein myeloid differentiation primary response protein 88 (MYD88). This interaction in turn activates mitogen-activated protein kinases (MAPKs) and NF-kB. TLR9 also induces the production of type I interferons by activation of IRF7 via IkB kinase alpha (IKKalpha) in plasmacytoid dendritic cells (pDCs). Various other DNA immune receptors, including IFI16 (IFN gamma inducible protein 16), cGAS (cyclic GMP-AMP synthase, DDX41 (DEAD box helicase 41), and ZBP1 (Z-DNA binding protein 1), interact with STING (stimulator of IFN genes) and activate the IKK complex and IRF3 via TBK1 (TANK-binding kinase 1). ZBP1 mediates N-terminal activation through the recruitment of RIP1 and RIP3 (receptor interacting protein 1 and 3, respectively). It also activates F-kB. The helicase DHX36 (DEAH box helicase 36) interacts with TRID in a complex to induce NF-kB and IRF-3 / 7, and the DHX9 helicase stimulates MYD88-dependent signaling in plasmacytoid dendritic cells. The DNA sensor LRRFIP1 (leucine-rich repeat Flightless interacting protein) complexes with β-catenin to activate the transcription of IRF3, whereas AIM2 (absent in melanoma 2) (absent in melanoma 2) recruits the adaptor protein ASC (apoptotic speck-like protein) to induce the caspase-1-activated inflammasome complex that leads to the secretion of interleukin-1 beta (IL1 beta) and IL18 [see Gasser S. et al. (2017) ibid., Fig. 1, which provides a schematic overview of DNA damage and DNA sensor pathways that lead to the production of inflammatory cytokines and the expression of ligands that activate innate immune receptors. Members of the non-homologous end joining pathway (orange), homologous recombination (red), inflammasome (dark green), NF-kB and interferon response (light green) are shown].

[0022] The factors and mechanisms involved in the activation of DNA sensor pathways in cancer are currently poorly understood. It will be important to identify intratumoral DNA species, sensors and pathways that are involved in the expression of IFN in different cancer types at all stages of the disease. In addition to being therapeutic targets in cancer, such factors also have prognostic and predictive value. Novel DNA sensor pathway agonists and antagonists are currently being developed and tested in preclinical trials. Such compounds will be useful in characterizing the role of DNA sensor pathways in the pathogenesis of cancer, autoimmune diseases and potentially other diseases.

[0023] T cell receptor signaling genes The immune response against pathogens can be triggered at different stages: There is a physical barrier, such as the skin, to keep out invaders. If it is breached, a first, fast, non-specific response, the innate immune response, kicks in. If this is insufficient, the adaptive immune response is triggered, which is much more specific, but takes longer to develop if the pathogen is encountered for the first time. Lymphocytes become activated by interacting with activated antigen-presenting cells from the innate immune system. They are also involved in maintaining memory to respond faster the next time the same pathogen is encountered.

[0024] Once activated, lymphocytes become highly specific and effective, so they undergo negative selection for their ability to recognize themselves, a process known as central tolerance. Because not all self-antigens are expressed at the site of selection, mechanisms of peripheral tolerance have developed as well, such as TCR ligation in the absence of costimulation, expression of inhibitory co-receptors, and suppression by Tregs. Disturbances in the balance between activation and suppression can lead to autoimmune disorders, or immune deficiencies and cancer, respectively.

[0025] T cell activation can have different functional outcomes depending on the location and type of T cell involved: CD8+ T cells differentiate into cytotoxic effector cells, while CD4+ T cells can differentiate into Th1 (secreting IFNγ and promoting cell-mediated immunity) or Th2 (secreting IL4 / 5 / 13 and promoting B cell and humoral immunity). Differentiation into other more recently identified T cell subsets, such as Tregs, which have an inhibitory effect on immune activation, is also possible (see Mosenden R. and Tasken K., "Cyclic AMP-mediated immune regulation - Overview of mechanisms of action in T-cells", Cell Signal, Vol. 23, No. 6, pp. 1009-1016, 2011, in particular Figure 4, T cell activation and its modulation by PKA, 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).

[0026] Both PKA and PDE4 regulated signaling intersect with TCR-induced T cell activation, fine tuning its regulation with opposing effects (see Abrahamsen H. et al., "TCR- and CD28-mediated recruitment of phosphodiesterase 4 to lipid rafts potentiates TCR signaling," J Immunol, Vol. 173, pp. 4847-4848, 2004, in particular Figure 6 showing the opposing actions of PKA and PDE4 in TCR activation). The molecule that connects these effectors is cyclic AMP (cAMP), an intracellular second messenger of the action of extracellular ligands. Within T cells, cAMP mediates the effects of prostaglandins, adenosine, histamine, beta-adrenergic agonists, neuropeptide hormones and beta-endorphin. Binding of these extracellular molecules to GPCRs leads to conformational changes in the GPCRs and the release of stimulatory subunits, followed by activation of adenylyl cyclase (AC), which hydrolyzes ATP to cAMP (see Abrahamsen H. et al., 2004, ibid., Fig. 6). PKA is the major, though not the only, effector of cAMP signaling (see Mosenden R. and Tasken K., 2011, ibid.; Tasken K. and Ruppelt A., 2006, ibid.). At the functional level, increased levels of cAMP lead to reduced production of IFNγ and IL-2 in T cells (see Abrahamsen H. et al., 2004, ibid.). In addition to blocking TCR activation, PKA has many more effects (see Figure 15 in "Immunity Leashed-Mechanisms of Regulation in the Human Immune System" by Torheim EA, PhD thesis, The Biotechnology Centre of Ola, University of Oslo, Norway, 2009).

[0027] In naive T cells, the hyperphosphorylated form of PAG targets Csk to lipid rafts. PKA targets Csk via the ezrin-EBP50-PAG scaffolding complex. By being specifically phosphorylated by PKA, Csk can negatively regulate Lck and Fyn to attenuate their activity and downregulate T cell activation (see Abrahamsen H. et al., 2004, ibid., fig. 6). Upon TCR activation, PAG is dephosphorylated and Csk is released from rafts. Dissociation of Csk is necessary for T cell activation to proceed. During the same time course, a Csk-G3BP complex appears to form, which sequesters Csk out of lipid rafts (see Mosenden R. and Tasken K., 2011, ibid. and Tasken K. and Ruppelt A., 2006, ibid.).

[0028] On the other hand, combined stimulation of TCR and CD28 mediates the recruitment of cyclic nucleotide phosphodiesterase PDE4 to lipid rafts, which enhances the degradation of cAMP (see Abrahamsen H. et al., 2004, ibid., fig. 6), thereby blocking TCR-induced cAMP production and enhancing the immune response of T cells. Upon TCR stimulation alone, recruitment of PDE4 is too low to sufficiently reduce cAMP levels, and therefore maximal activation of T cells cannot occur (see Abrahamsen H. et al., 2004, ibid.).

[0029] Thus, by actively suppressing proximal TCR signaling, cAMP-PKA-Csk-mediated signaling appears to set a threshold for T cell activation. Recruitment of PDEs can block this suppression. Tissue- or cell type-specific regulation is achieved by expression of multiple isoforms of AC, PKA and PDE. As mentioned above, the balance between activation and suppression needs to be tightly regulated to prevent the development of autoimmune disorders, immune deficiencies and cancer.

[0030] PDE4D7-related genes Phosphodiesterases (PDEs) provide the only means to degrade the second messenger 3'-5' cyclic AMP. Thus, phosphodiesterases provide an important regulatory role. Thus, abnormal changes in phosphodiesterase expression, activity, and subcellular localization are all implicated in the molecular pathology underlying certain disease states. Indeed, enrichment of mutations within PDE genes in prostate cancer patients has recently been shown to result in elevated cAMP signaling and a potential predisposition to prostate cancer. However, the varied expression profiles in different cell types coupled with the complex array of isoform variants within each PDE family make it difficult to understand the link between abnormal changes in PDE expression and functionality during disease progression. Several studies have attempted to describe the complement of PDEs in the prostate, all of which identified significant PDE4 expression levels in parallel with other PDEs, thus leading to the development of the PDE4D7 biomarker (see Alves de Inda M. et al., Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes, Eur Urol Focus, Vol. 4, No. 3, pp. 376-384, 2018). Since the PDE4D7 biomarker had proven to be a good predictor, we hypothesized that the ability to identify markers highly correlated with the PDE47 biomarker would also be useful in prognosticating outcome in certain cancer subjects.

[0031] Gene selection A list of genes was initially selected for predictive value in prostate cancer subjects, which are shown in this document to also have predictive value for outcome in colorectal cancer subjects.

[0032] The identified immune defense response genes, ZBP1, and AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, were identified as follows: A group of 538 prostate cancer patients were treated with RP and prostate cancer tissues were stored 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 treatment (SRT), salvage androgen deprivation treatment (SADT), chemotherapy (CTX)). For each of these patients, a PDE4D7 score was calculated and categorized into four PDE4D7 score classes (see Alves de Inda M. et al., 2018, supra). PDE4D7 score class 1 corresponds to the patient sample with the lowest expression level of PDE4D7, while PDE4D7 score class 4 corresponds to the patient sample with the highest level of PDE4D7 expression.Then, the RNASeq expression data (TPM-transcripts per million) of 538 prostate cancer subjects was examined for differential gene expression between PDE4D7 score class 1 and class 4.Specifically, for approximately 20,000 protein-coding transcripts, it was determined whether the average expression level of patients with PDE4D7 score class 1 is more than twice that of patients with PDE4D7 score class 4.This analysis resulted in 637 genes with a PDE4D7 score class 1 / PDE4D7 score class 4 ratio of more than 2, with the lowest average expression in each of the four PDE4D7 score classes being 1 TPM. These 637 genes were then further subjected to molecular pathway analysis (www.david.ncifcrf.gov), resulting in various enriched annotation clusters. Annotation cluster #2 demonstrated enrichment in 30 genes (enrichment score: 10.8) with functions in defense responses against viruses, negative regulation of viral genome replication, and type I interferon signaling.Further heatmap analysis confirmed that these immune defense response genes were generally more highly expressed in samples from patients in PDE4D7 score class 1 than in samples from patients in PDE4D7 score class 4. Classes of genes that function in defense response against viruses, negative regulation of viral genome replication, and type I interferon signaling were further enriched to 61 genes by literature search to identify additional genes with the same molecular function. Further selection from the 61 genes was performed based on their combinatorial power to separate patients who died from prostate cancer from those who did not, resulting in a favorable set of 14 genes. The number of events (metastasis, prostate cancer-specific death) was found to be enriched in the subcohort that low-expressed these genes compared to the entire patient cohort (538 patients) and a subcohort of 151 patients who received salvage RT (SRT) after disease recurrence after surgery.

[0033] 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 tissues were stored 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 treatment (SRT), salvage androgen deprivation treatment (SADT), chemotherapy (CTX)). For each of these patients, a PDE4D7 score was calculated and categorized into four PDE4D7 score classes (see Alves de Inda M. et al., 2018, supra). PDE4D7 score class 1 corresponds to the patient sample with the lowest expression level of PDE4D7, while PDE4D7 score class 4 corresponds to the patient sample with the highest level of PDE4D7 expression.Then, the RNASeq expression data (TPM-transcripts per million) of 538 prostate cancer subjects was examined for differential gene expression between PDE4D7 score class 1 and class 4.Specifically, for approximately 20,000 protein-coding transcripts, it was determined whether the average expression level of patients with PDE4D7 score class 1 is more than twice that of patients with PDE4D7 score class 4.This analysis resulted in 637 genes with a PDE4D7 score class 1 / PDE4D7 score class 4 ratio of more than 2, with the lowest average expression in each of the four PDE4D7 score classes being 1 TPM. These 637 genes were then further subjected to molecular pathway analysis (www.david.ncifcrf.gov), resulting in various enriched annotation clusters. Annotation cluster #6 showed enrichment in 17 genes (enrichment score: 5.9) with functions in primary immune deficiency and activation of T cell receptor signaling.Further heatmap analysis confirmed that these T cell receptor signaling genes were generally more highly expressed in samples from patients in PDE4D7 score class 1 than in samples from patients in PDE4D7 score class 4.

[0034] The identified PDE4D7-related genes ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2 were identified as follows: In RNAseq data of approximately 60,000 transcripts generated in 571 prostate cancer patients, a set of genes was identified within the data that correlated with the expression of the known biomarker PDE4D7. Expression correlation between any of these genes and PDE4D7 across the 571 samples was performed by Pearson correlation and expressed as a value between 0 and 1 for a positive correlation or between -1 and 0 for an inverse correlation. As input data for calculating the correlation coefficient, PDE4D7 scores (see Alves de Inda M. et al., 2018, supra) and RNAseq, which determined the TPM gene expression value per gene of interest (see below).

[0035] The maximum inverse correlation coefficient identified between the expression of any of approximately 60,000 transcripts and the expression of PDE4D7 was -0.38, and the maximum positive correlation coefficient identified between the expression of any of approximately 60,000 transcripts and the expression of PDE4D7 was +0.56. Genes with correlations ranging from -0.31 to -0.38 and +0.41 to +0.56 were selected. A total of 77 transcripts fitting these characteristics were identified. From these 77 transcripts, eight PDE4D7-related genes were selected: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, by repeated testing of Cox regression combination models in a subcohort of 186 patients who underwent salvage radiation treatment (SRT) for biochemical recurrence after surgery. The clinical endpoint tested was prostate cancer-specific death after initiating SRT. The boundary condition for the selection of the eight genes was given by restricting the p-value in the multivariate Cox regression to be less than 0.1 for all genes retained in the model.

[0036] The term "ABCC5" refers to the nucleotide sequence as set forth in SEQ ID NO: 1 or SEQ ID NO: 2 corresponding to the sequence of the human ATP-binding cassette subfamily C member 5 gene (Ensembl: ENSG00000114770), e.g. as defined in NCBI Reference Sequence NM_001023587.2 or NCBI Reference Sequence NM_005688.3, ​​in particular the NCBI Reference Sequence for the ABCC5 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 3 or SEQ ID NO: 4 corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001018881.1 and NCBI Protein Accession Reference Sequence NP_005679 which encodes the ABCC5 polypeptide.

[0037] The term "ABCC5" also refers to a nucleotide sequence that exhibits high homology to ABCC5, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:1 or SEQ ID NO:2, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:3 or SEQ ID NO:4. This includes an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:3 or SEQ ID NO:4, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:1 or SEQ ID NO:2.

[0038] The term "AIM2" refers to the nucleotide sequence as set forth in SEQ ID NO: 5, which corresponds to the sequence of the absent in melanoma 2 gene (Ensembl: ENSG00000163568), e.g. as defined in the NCBI Reference Sequence NM_004833, in particular the sequence of the NCBI Reference Sequence for the AIM2 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 6, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_004824, which encodes the AIM2 polypeptide.

[0039] The term "AIM2" also refers to a nucleotide sequence that exhibits high homology to AIM2, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:5, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:6. 6, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:6, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:5.

[0040] The term "APOBEC3A" refers to the nucleotide sequence as set forth in SEQ ID NO: 7, which corresponds to the sequence of the Apolipoprotein B mRNA editing enzyme catalytic subunit 3A gene (Ensembl: ENSG00000128383), e.g. the sequence defined in the NCBI reference sequence NM_145699, in particular the sequence of the NCBI reference sequence of the APOBEC3A transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 8, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_663745 that encodes the APOBEC3A polypeptide.

[0041] The term "APOBEC3A" also refers to a nucleotide sequence that exhibits high homology to APOBEC3A, e.g., a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:7, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:8. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:8, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:7.

[0042] The term "CD2" refers to the nucleotide sequence as set forth in SEQ ID NO: 9, which corresponds to the sequence of the Cluster of Differentiation 2 gene (Ensembl: ENSG00000116824), e.g. as defined in the NCBI Reference Sequence NM_001767, in particular the sequence of the NCBI Reference Sequence for the CD2 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 10, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_001758, which encodes the CD2 polypeptide.

[0043] The term "CD2" also refers to a nucleotide sequence that exhibits high homology to CD2, e.g., a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:9, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:10. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:10, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:9.

[0044] The term "CD247" refers to the nucleotide sequence as set forth in SEQ ID NO: 11 or SEQ ID NO: 12, which corresponds to the sequence of the Cluster of Differentiation 247 gene (Ensembl: ENSG00000198821), e.g. as defined in NCBI Reference Sequence NM_000734 or NCBI Reference Sequence NM_198053, in particular the NCBI Reference Sequence of the CD247 transcript shown above, and it also relates to the corresponding amino acid sequence, e.g. as set forth in SEQ ID NO: 13 or SEQ ID NO: 14, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_000725 and NCBI Protein Accession Reference Sequence NP_932170, which encodes a CD247 polypeptide.

[0045] The term "CD247" also refers to a nucleotide sequence exhibiting high homology to CD247, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:11 or SEQ ID NO:12, or an nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:13 or SEQ ID NO:14. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:13 or SEQ ID NO:14, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:11 or SEQ ID NO:12.

[0046] The term "CD28" refers to the nucleotide sequence as set forth in SEQ ID NO: 15 or SEQ ID NO: 16, which corresponds to the sequence of the Cluster of Differentiation 28 gene (Ensembl: ENSG00000178562), e.g. as defined in NCBI Reference Sequence NM_006139 or NCBI Reference Sequence NM_001243078, in particular the NCBI Reference Sequence of the CD28 transcript shown above, and it also relates to the corresponding amino acid sequence, e.g. as set forth in SEQ ID NO: 17 or SEQ ID NO: 18, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_006130 and NCBI Protein Accession Reference Sequence NP_001230007, which encodes the CD28 polypeptide.

[0047] The term "CD28" also refers to a nucleotide sequence that exhibits high homology to CD28, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:15 or SEQ ID NO:16, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:17 or SEQ ID NO:18. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:17 or SEQ ID NO:18, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:15 or SEQ ID NO:16.

[0048] The term "CD3E" refers to the nucleotide sequence as set forth in SEQ ID NO: 19, which corresponds to the sequence of the Cluster of Differentiation 3E gene (Ensembl: ENSG00000198851), e.g. as defined in the NCBI Reference Sequence NM_000733, in particular the sequence of the NCBI Reference Sequence of the CD3E transcript shown above, and it also relates to the corresponding amino acid sequence, e.g. as set forth in SEQ ID NO: 20, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_000724, which encodes the CD3E polypeptide.

[0049] The term "CD3E" also refers to a nucleotide sequence that exhibits high homology to CD3E, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 19, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 20. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:20, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:19.

[0050] The term "CD3G" refers to the nucleotide sequence as set forth in SEQ ID NO: 21, which corresponds to the sequence of the Cluster of Differentiation 3G gene (Ensembl: ENSG00000160654), e.g. as defined in the NCBI Reference Sequence NM_000073, in particular the sequence of the NCBI Reference Sequence of the CD3G transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 22, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_000064, which encodes the CD3G polypeptide.

[0051] The term "CD3G" also refers to a nucleotide sequence that exhibits high homology to CD3G, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:21, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:22. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:22, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:21.

[0052] The term "CD4" refers to the nucleotide sequence as set forth in SEQ ID NO: 23, which corresponds to the sequence of the Cluster of Differentiation 4 gene (Ensembl: ENSG00000010610), e.g. as defined in the NCBI Reference Sequence NM_000616, in particular the sequence of the NCBI Reference Sequence for the CD4 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 24, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_000607, which codes for the CD4 polypeptide.

[0053] The term "CD4" also refers to a nucleotide sequence that exhibits high homology to CD4, e.g., a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:23, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:24. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:24, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:23.

[0054] The term "CIAO1" refers to the nucleotide sequence as set forth in SEQ ID NO: 25, which corresponds to the sequence of the NCBI Reference Sequence NM_004804, in particular the NCBI Reference Sequence of the CIAO1 transcript shown above, of the gene for Cytoplasmic Iron-Sulfur Assembly Component 1 (Ensembl: ENSG00000144021), and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 26, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_663745 that encodes the CIAO1 polypeptide.

[0055] The term "CIAO1" also refers to a nucleotide sequence that exhibits high homology to CIAO1, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:25, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:26. 25. The present invention relates to a nucleic acid sequence encoding an amino acid sequence, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:26, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:25.

[0056] The term "CSK" refers to the nucleotide sequence as set forth in SEQ ID NO: 27, which corresponds to the sequence of the gene for C-terminal Src kinase (Ensembl: ENSG00000103653), e.g. as defined in the NCBI reference sequence NM_004383, in particular the sequence of the NCBI reference sequence for the CSK transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 28, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_004374, which encodes a CSK polypeptide.

[0057] The term "CSK" also refers to a nucleotide sequence that exhibits high homology to CSK, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:27, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:28. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:28, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:27.

[0058] The term "CUX2" refers to the nucleotide sequence as set forth in SEQ ID NO: 29, which corresponds to the sequence of the human Cut-like homeobox 2 gene (Ensembl: ENSG00000111249), e.g. as defined in NCBI Reference Sequence NM_015267.3, in particular the sequence of the NCBI Reference Sequence for the CUX2 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 30, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_056082.2, which encodes a CUX2 polypeptide.

[0059] The term "CUX2" also refers to a nucleotide sequence that exhibits high homology to CUX2, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:29, or an nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:30. The present invention includes 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 the sequence set forth in SEQ ID NO:30, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:29.

[0060] The term "DDX58" refers to the nucleotide sequence as set forth in SEQ ID NO: 31, which corresponds to the sequence of the DExD / H-box helicase 58 gene (Ensembl: ENSG00000107201), e.g. as defined in the NCBI Reference Sequence NM_014314, in particular the sequence of the NCBI Reference Sequence for the DDX58 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 32, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_055129, which encodes the DDX58 polypeptide.

[0061] The term "DDX58" also refers to a nucleotide sequence that exhibits high homology to DDX58, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:31, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:32. 31. The present invention relates to a nucleic acid sequence encoding an amino acid sequence, or a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:32, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:31.

[0062] The term "DHX9" refers to the nucleotide sequence as set forth in SEQ ID NO: 33, which corresponds to the sequence of the DExD / H-box helicase 9 gene (Ensembl: ENSG00000135829), e.g. as defined in the NCBI Reference Sequence NM_001357, in particular the sequence of the NCBI Reference Sequence for the DHX9 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 34, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_001348 encoding the DHX9 polypeptide.

[0063] The term "DHX9" also refers to a nucleotide sequence that exhibits high homology to DHX9, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:33, or an nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:34. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:34, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:33.

[0064] The term "EZR" refers to the nucleotide sequence as set forth in SEQ ID NO: 35, which corresponds to the sequence of the Ezrin gene (Ensembl: ENSG00000092820), e.g. the sequence defined in the NCBI reference sequence NM_003379, in particular the sequence of the NCBI reference sequence of the EZR transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 36, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_003370 that encodes the EZR polypeptide.

[0065] The term "EZR" also refers to a nucleotide sequence that exhibits high homology to EZR, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:35, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:36. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:36, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:35.

[0066] The term "FYN" refers to the nucleotide sequence as set forth in SEQ ID NO: 37, SEQ ID NO: 38 or SEQ ID NO: 39, corresponding to the sequence of the NCBI Reference Sequence of the FYN transcript as set forth above, of the gene of the FYN Proto-Oncogene (Ensembl: ENSG00000010810), for example as defined in the NCBI Reference Sequence NM_002037, NCBI Reference Sequence NM_153047 or NCBI Reference Sequence NM_153048, in particular the NCBI Reference Sequence of the FYN transcript as set forth above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 40, SEQ ID NO: 41 or SEQ ID NO: 42, corresponding to the protein sequence as defined in the NCBI Protein Accession Reference Sequence NP_002028, NCBI Protein Accession Reference Sequence NP_694592, NCBI Protein Accession Reference Sequence XP_005266949, which encodes the FYN polypeptide.

[0067] 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 a sequence as set forth in SEQ ID NO:37, SEQ ID NO:38 or SEQ ID NO:39, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:40, SEQ ID NO:41 or SEQ ID NO:42. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:40, SEQ ID NO:41 or SEQ ID NO:42, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:37, SEQ ID NO:38 or SEQ ID NO:39.

[0068] The term "IFI16" refers to the nucleotide sequence as set forth in SEQ ID NO: 43, which corresponds to the sequence of the gene for interferon gamma inducible protein 16 (Ensembl: ENSG00000163565), e.g. as defined in NCBI Reference Sequence NM_005531, in particular the sequence of the NCBI Reference Sequence for the IFI16 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 44, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_005522, which encodes the IFI16 polypeptide.

[0069] The term "IFI16" also refers to a nucleotide sequence that exhibits high homology to IFI16, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 43, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 44. 44、55%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:44, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:43.

[0070] The term "IFIH1" refers to the nucleotide sequence as set forth in SEQ ID NO: 45, which corresponds to the sequence of the Interferon Induced With Helicase C Domain 1 gene (Ensembl: ENSG00000115267), e.g. as defined in NCBI Reference Sequence NM_022168, in particular the NCBI Reference Sequence of the IFIH1 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 46, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_071451, which encodes an IFIH1 polypeptide.

[0071] The term "IFIH1" also refers to a nucleotide sequence that exhibits high homology to IFIH1, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 45, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 46. 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 as set forth in SEQ ID NO:46, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:45.

[0072] The term "IFIT1" refers to the nucleotide sequence as set forth in SEQ ID NO: 47 or SEQ ID NO: 48, which corresponds to the sequence of the NCBI Reference Sequence NM_001270929 or NCBI Reference Sequence NM_001548.5, in particular the NCBI Reference Sequence of the IFIT1 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 49 or SEQ ID NO: 50, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001257858 and NCBI Protein Accession Reference Sequence NP_001539, which encodes the IFIT1 polypeptide.

[0073] The term "IFIT1" also refers to a nucleotide sequence that exhibits high homology to IFIT1, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 47 or SEQ ID NO: 48, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 49 or SEQ ID NO: 50. 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 a sequence as set forth in SEQ ID NO:49 or SEQ ID NO:50, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:47 or SEQ ID NO:48.

[0074] The term "IFIT3" refers to the nucleotide sequence as set forth in SEQ ID NO: 51, which corresponds to the sequence of the NCBI Reference Sequence NM_001031683, in particular the NCBI Reference Sequence of the IFIT3 transcript shown above, of the gene for tetratricopeptide repeat-containing interferon-inducible protein 3 (Ensembl: ENSG00000119917), and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 52, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_001026853, which encodes an IFIT3 polypeptide.

[0075] The term "IFIT3" also refers to a nucleotide sequence that exhibits high homology to IFIT3, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:51, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:52. 51。

[0043] The present invention includes 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 the sequence as set forth in SEQ ID NO:52, or a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:51.

[0076] The term "KIAA1549" refers to the nucleotide sequence as set forth in SEQ ID NO: 53 or SEQ ID NO: 54, corresponding to the sequence of the human KIAA1549 gene (Ensembl: ENSG00000122778), e.g. as defined in NCBI Reference Sequence NM_020910 or NCBI Reference Sequence NM_001164665, in particular the sequence of the NCBI Reference Sequence of the KIAA1549 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 55 or SEQ ID NO: 56, corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_065961 and NCBI Protein Accession Reference Sequence NP_001158137, which encodes the KIAA1549 polypeptide.

[0077] The term "KIAA1549" also refers to a nucleotide sequence that exhibits high homology to KIAA1549, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:53 or SEQ ID NO:54, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:55 or SEQ ID NO:56. or a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:55 or SEQ ID NO:56, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:53 or SEQ ID NO:54.

[0078] The term "LAT" refers to the nucleotide sequence as set forth in SEQ ID NO: 57 or SEQ ID NO: 58, which corresponds to the sequence of the NCBI Reference Sequence of the LAT transcript shown above, of the gene Linker For Activation Of T-Cells (Ensembl: ENSG00000213658), e.g. as defined in NCBI Reference Sequence NM_001014987 or NCBI Reference Sequence NM_014387, in particular to the corresponding amino acid sequence as set forth in SEQ ID NO: 59 or SEQ ID NO: 60, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001014987 and NCBI Protein Accession Reference Sequence NP_055202, which encodes a LAT polypeptide.

[0079] 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 as set forth in SEQ ID NO:57 or SEQ ID NO:58, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:59 or SEQ ID NO:60. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:59 or SEQ ID NO:60, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:57 or SEQ ID NO:58.

[0080] The term "LCK" refers to the nucleotide sequence as set forth in SEQ ID NO: 61, which corresponds to the sequence of the gene of the LCK Proto-Oncogene (Ensembl: ENSG00000182866), e.g. as defined in the NCBI Reference Sequence NM_005356, in particular the NCBI Reference Sequence of the LCK transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 62, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_005347, which encodes the LCK polypeptide.

[0081] The term "LCK" also refers to a nucleotide sequence that exhibits high homology to LCK, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:61, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:62. or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:62, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:61.

[0082] The term "LRRFIP1" refers to the nucleotide sequence as defined in SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65 or SEQ ID NO: 66, which corresponds to the sequence defined in the gene for LRR-binding FLII interacting protein 1 (Ensembl: ENSG00000124831), for example NCBI Reference Sequence NM_004735, NCBI Reference Sequence NM_001137550, NCBI Reference Sequence NM_001137553 or NCBI Reference Sequence NM_001137552, in particular the NCBI Reference Sequence for the LRRFIP1 transcript shown above. The term "LRRFIP1" refers to the peptide sequence of the LRRFIP1 polypeptide, which also relates to the corresponding amino acid sequence as set forth in, for example, SEQ ID NO:67, SEQ ID NO:68, SEQ ID NO:69 or SEQ ID NO:70, corresponding to the protein sequence defined in NCBI protein accession reference sequence NP_004726, NCBI protein accession reference sequence NP_001131022, NCBI protein accession reference sequence NP_001131025 and NCBI protein accession reference sequence NP_001131024 which encodes the LRRFIP1 polypeptide.

[0083] The term "LRRFIP1" also refers to a nucleotide sequence that exhibits high homology to LRRFIP1, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:63, SEQ ID NO:64, SEQ ID NO:65 or SEQ ID NO:66, or a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:67, SEQ ID NO:68, SEQ ID NO:69 or SEQ ID NO:70. a nucleic acid sequence encoding an amino acid sequence 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 a sequence as set forth in SEQ ID NO:67, SEQ ID NO:68, SEQ ID NO:69 or SEQ ID NO:70, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:63, SEQ ID NO:64, SEQ ID NO:65 or SEQ ID NO:66.

[0084] The term "MYD88" refers to the MYD88 innate immune signaling adaptor gene (Ensembl: ENSG00000172936), e.g., the sequence defined in NCBI Reference Sequence NM_001172567, NCBI Reference Sequence NM_001172568, NCBI Reference Sequence NM_001172569, NCBI Reference Sequence NM_001172566 or NCBI Reference Sequence NM_002468, specifically the nucleotide sequence as set forth in SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74 or SEQ ID NO: 75, which correspond to the sequence of the NCBI Reference Sequence for the MYD88 transcript shown above. and also to the corresponding amino acid sequence as set forth in SEQ ID NO:76, SEQ ID NO:77, SEQ ID NO:78, SEQ ID NO:79 or SEQ ID NO:80, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_001166038, NCBI protein accession reference sequence NP_001166039, NCBI protein accession reference sequence NP_001166040, NCBI protein accession reference sequence NP_001166037 and NCBI protein accession reference sequence NP_002459 encoding a MYD88 polypeptide.

[0085] The term "MYD88" also refers to a nucleotide sequence that exhibits high homology to MYD88, e.g., a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ ID NO:74 or SEQ ID NO:75, or a sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:76, SEQ ID NO:77, SEQ ID NO:78, SEQ ID NO:79 or SEQ ID NO:80. 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 a sequence as set forth in SEQ ID NO:76, SEQ ID NO:77, SEQ ID NO:78, SEQ ID NO:79 or SEQ ID NO:80, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ ID NO:74 or SEQ ID NO:75.

[0086] The term "OAS1" refers to the nucleotide sequence as set forth in SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83 or SEQ ID NO: 84, which corresponds to the sequence of the NCBI Reference Sequence of the OAS1 transcript shown above, in the gene of 2'-5' oligoadenylate synthetase 1 (Ensembl: ENSG00000089127), e.g., the sequence defined in NCBI Reference Sequence NM_001320151, NCBI Reference Sequence NM_002534, NCBI Reference Sequence NM_001032409 or NCBI Reference Sequence NM_016816. "OAS1" refers to a sequence corresponding to the protein sequence defined in NCBI protein accession reference sequence NP_001307080, NCBI protein accession reference sequence NP_002525, NCBI protein accession reference sequence NP_001027581, and NCBI protein accession reference sequence NP_058132 that encodes an OAS1 polypeptide, and also to the corresponding amino acid sequence as set forth in, e.g., SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87, or SEQ ID NO:88.

[0087] The term "OAS1" also refers to a nucleotide sequence that exhibits high homology to OAS1, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:81, SEQ ID NO:82, SEQ ID NO:83 or SEQ ID NO:84, or an nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87 or SEQ ID NO:88. The present invention includes a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87 or SEQ ID NO:88, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:81, SEQ ID NO:82, SEQ ID NO:83 or SEQ ID NO:84.

[0088] The term "PAG1" refers to the nucleotide sequence as set forth in SEQ ID NO: 89, which corresponds to the sequence of the Phosphoprotein Membrane Anchor With Glycosphingolipid Microdomains 1 gene (Ensembl: ENSG00000076641), e.g. as defined in the NCBI Reference Sequence NM_018440, in particular the sequence of the NCBI Reference Sequence for the PAG1 transcript shown above, and it also relates to the corresponding amino acid sequence, e.g. as set forth in SEQ ID NO: 90, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_060910, which encodes the PAG1 polypeptide.

[0089] The term "PAG1" also refers to a nucleotide sequence that exhibits high homology to PAG1, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:89, or an nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:90. The present invention includes 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 the sequence set forth in SEQ ID NO:90, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:89.

[0090] The term "PDE4D" refers to the gene for human phosphodiesterase 4D (Ensembl: ENSG00000113448), e.g., NCBI Reference Sequence NM_001104631, NCBI Reference Sequence NM_001349242, NCBI Reference Sequence NM_001197218, NCBI Reference Sequence NM_006203, NCBI Reference Sequence NM_001197221, NCBI Reference Sequence NM_001197220, NCBI Reference Sequence NM_0011972 23, NCBI Reference Sequence NM_001165899, or the sequence defined in NCBI Reference Sequence NM_001165899, specifically the nucleotide sequence as set forth in SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98 or SEQ ID NO: 99, which corresponds to the sequence of the NCBI Reference Sequence of the PDE4D transcript shown above, and which also refers to the NCBI Reference Sequence NM_001165899 that encodes a PDE4D polypeptide. It also relates to the corresponding amino acid sequences as set forth in, for example, SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107 or SEQ ID NO:108, which correspond to the protein sequences defined in protein accession reference sequence NP_001098101, NCBI protein accession reference sequence NP_001336171, NCBI protein accession reference sequence NP_001184147, NCBI protein accession reference sequence NP_006194, NCBI protein accession reference sequence NP_001184150, NCBI protein accession reference sequence NP_001184149, NCBI protein accession reference sequence NP_001184152, NCBI protein accession reference sequence NP_001159371 and NCBI protein accession reference sequence NP_001184148.

[0091] The term "PDE4D" also refers to a nucleotide sequence that exhibits high homology to PDE4D, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:91, SEQ ID NO:92, SEQ ID NO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:97, SEQ ID NO:98, or SEQ ID NO:99, or a sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, or SEQ ID NO:108. or a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107 or SEQ ID NO:108, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:91, SEQ ID NO:92, SEQ ID NO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:97, SEQ ID NO:98 or SEQ ID NO:99.

[0092] The term "PRKACA" refers to the nucleotide sequence as set forth in SEQ ID NO: 109 or SEQ ID NO: 110, which corresponds to the sequence of the NCBI Reference Sequence of the PRKACA transcript shown above, of the gene of Protein Kinase cAMP-Activated Catalytic Subunit Alpha (Ensembl: ENSG00000072062), e.g. as defined in NCBI Reference Sequence NM_002730 or NCBI Reference Sequence NM_207518, and in particular to the corresponding amino acid sequence as set forth in SEQ ID NO: 111 or SEQ ID NO: 112, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_002721 and NCBI Protein Accession Reference Sequence NP_997401, which encodes a PRKACA polypeptide.

[0093] The term "PRKACA" also refers to a nucleotide sequence that exhibits high homology to PRKACA, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:109 or SEQ ID NO:110, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:111 or SEQ ID NO:112. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:111 or SEQ ID NO:112, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:109 or SEQ ID NO:110.

[0094] The term "PRKACB" refers to the gene for Protein Kinase cAMP-Activated Catalytic Subunit Beta (Ensembl: ENSG00000142875), e.g., NCBI Reference Sequence NM_002731, NCBI Reference Sequence NM_182948, NCBI Reference Sequence NM_001242860, NCBI Reference Sequence NM_001242859, NCBI Reference Sequence NM_001242858, NCBI Reference Sequence NM_001242862, NCBI Reference Sequence NM_001242861, NCBI Reference Sequence NM_001300915, NCBI Reference Sequence NM_207578, NCBI Reference Sequence NM_001242863, NCBI Reference Sequence NM_001242864, NCBI Reference Sequence NM_001242865, NCBI Reference Sequence NM_001242866, NCBI Reference Sequence NM_001242867, NCBI Reference Sequence NM_001242869, NCBI Reference Sequence NM_001242870, NCBI Reference Sequence NM_001242871, NCBI Reference Sequence NM_001242872, NCBI Reference Sequence NM_001242873, NCBI Reference Sequence NM_001242874, NCBI Reference Sequence NM_001242875, NCBI Reference Sequence NM_001242876, NCBI Reference Sequence NM_001242877, NCBI Reference Sequence NM_001242878, NCBI Reference Sequence NM_001242879, NCBI Reference Sequence NM_001242880 1242857 or the sequence defined in NCBI Reference Sequence NM_001300917, in particular the nucleotide sequence as set forth in SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO: 117, SEQ ID NO: 118, SEQ ID NO: 119, SEQ ID NO: 120, SEQ ID NO: 121, SEQ ID NO: 122 or SEQ ID NO: 123, which correspond to the sequence of the NCBI Reference Sequence for the PRKACB transcript shown above, and which also refers to the NCBI Protein Accession Reference Sequence NP_001300917 encoding the PRKACB polypeptide. _002722, NCBI protein accession reference sequence NP_891993, NCBI protein accession reference sequence NP_001229789, NCBI protein accession reference sequence NP_001229788, NCBI protein accession reference sequence NP_001229787, NCBI protein accession reference sequence NP_001229791, NCBI protein accession reference sequence NP_001229790, NCBI protein accession reference sequence NP_001287 844, NCBI protein accession reference sequence NP_997461, NCBI protein accession reference sequence NP_001229786 and NCBI protein accession reference sequence NP_001287846, e.g. as set forth in SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133 or SEQ ID NO:134.

[0095] The term "PRKACB" also refers to a nucleotide sequence that exhibits high homology to PRKACB, such as at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 100%, 101%, 102%, 103%, 104%, 105%, 106%, 107%, 108%, 109%, 200%, 210%, 211%, 212%, 213%, 214%, 215%, 216%, 217%, 218%, 219%, 220%, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, a nucleic acid sequence that is 6%, 97%, 98% or 99% identical to, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to, a sequence as set forth in SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133 or SEQ ID NO:134. or a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133 or SEQ ID NO:134; or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ ID NO:117, SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQ ID NO:122 or SEQ ID NO:123.

[0096] The term "PTPRC" refers to the nucleotide sequence as set forth in SEQ ID NO: 135 or SEQ ID NO: 136, which corresponds to the sequence of the NCBI Reference Sequence for the PTPRC transcript shown above, of the gene for Protein Tyrosine Phosphatase Receptor Type C (Ensembl: ENSG00000081237), e.g. as defined in NCBI Reference Sequence NM_002838 or NCBI Reference Sequence NM_080921, and in particular to the corresponding amino acid sequence as set forth in SEQ ID NO: 137 or SEQ ID NO: 138, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_002829, which encodes a PTPRC polypeptide, and to the corresponding amino acid sequence as set forth in SEQ ID NO: 137 or SEQ ID NO: 138, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_563578.

[0097] The term "PTPRC" also refers to a nucleotide sequence that exhibits high homology to PTPRC, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 135 or SEQ ID NO: 136, or an nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 137 or SEQ ID NO: 138. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:137 or SEQ ID NO:138, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:135 or SEQ ID NO:136.

[0098] The term "RAP1GAP2" refers to the nucleotide sequence as set forth in SEQ ID NO: 139, SEQ ID NO: 140 or SEQ ID NO: 141 of the gene for human RAP1 GTPase activating protein 2 (ENSG00000132359), e.g. the sequence defined in NCBI Reference Sequence NM_015085, NCBI Reference Sequence NM_001100398 or NCBI Reference Sequence NM_001330058, in particular the sequence of the NCBI Reference Sequence for the RAP1GAP2 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 142, SEQ ID NO: 143 or SEQ ID NO: 144, e.g. corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_055900, NCBI Protein Accession Reference Sequence NP_001093868 and NCBI Protein Accession Reference Sequence NP_001316987 which encodes a RAP1GAP2 polypeptide.

[0099] The term "RAP1GAP2" also refers to a nucleotide sequence that exhibits high homology to RAP1GAP2, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:139, SEQ ID NO:140 or SEQ ID NO:141, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:142, SEQ ID NO:143 or SEQ ID NO:144. or a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:142, SEQ ID NO:143 or SEQ ID NO:144, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:139, SEQ ID NO:140 or SEQ ID NO:141.

[0100] The term "SLC39A11" refers to the nucleotide sequence as set forth in SEQ ID NO: 145 or SEQ ID NO: 146, which corresponds to the sequence of the human solute carrier family 39 member 11 gene (Ensembl: ENSG00000133195), e.g. as defined in NCBI Reference Sequence NM_139177 or NCBI Reference Sequence NM_001352692, in particular the NCBI Reference Sequence of the SLC39A11 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 147 or SEQ ID NO: 148, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_631916 and NCBI Protein Accession Reference Sequence NP_001339621, which encodes the SLC39A11 polypeptide.

[0101] The term "SLC39A11" also refers to a nucleotide sequence that exhibits high homology to SLC39A11, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 145 or SEQ ID NO: 146, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 147 or SEQ ID NO: 148. or a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:147 or SEQ ID NO:148, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:145 or SEQ ID NO:146.

[0102] The term "TDRD1" refers to the nucleotide sequence as set forth in SEQ ID NO: 149, which corresponds to the sequence of the human Tudor domain-containing 1 gene (Ensembl: ENSG00000095627), e.g. as defined in the NCBI Reference Sequence NM_198795, in particular the sequence of the NCBI Reference Sequence for the TDRD1 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 150, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_942090, which encodes the TDRD1 polypeptide.

[0103] The term "TDRD1" also refers to a nucleotide sequence that exhibits high homology to TDRD1, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 149, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 150. 150、55%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:150, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:149.

[0104] The term "TLR8" refers to the nucleotide sequence as set forth in SEQ ID NO: 151 or SEQ ID NO: 152 corresponding to the sequence of the Toll-like receptor 8 gene (Ensembl: ENSG00000101916), e.g. as defined in NCBI Reference Sequence NM_138636 or NCBI Reference Sequence NM_016610, in particular the NCBI Reference Sequence of the TLR8 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 153 or SEQ ID NO: 154 corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_619542 and NCBI Protein Accession Reference Sequence NP_057694 which encodes the TLR8 polypeptide.

[0105] The term "TLR8" also refers to a nucleotide sequence that exhibits high homology to TLR8, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 151 or SEQ ID NO: 152, or an nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 153 or SEQ ID NO: 154. The present invention includes 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 the sequence as set forth in SEQ ID NO:153 or SEQ ID NO:154, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:151 or SEQ ID NO:152.

[0106] The term "VWA2" refers to the nucleotide sequence as set forth in SEQ ID NO: 155, which corresponds to the sequence of the human von Willebrand factor A domain containing 2 gene (Ensembl: ENSG00000165816), e.g. as defined in NCBI Reference Sequence NM_001320804, in particular the sequence of the NCBI Reference Sequence for the VWA2 transcript shown above, and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 156, which corresponds to the protein sequence defined in NCBI protein accession Reference Sequence NP_001307733, which encodes the VWA2 polypeptide.

[0107] The term "VWA2" also refers to a nucleotide sequence that exhibits high homology to VWA2, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 155, or an nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 156. 155。 Amino acid sequence encoding a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 156, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 155.

[0108] The term "ZAP70" refers to the nucleotide sequence as set forth in SEQ ID NO: 157 or SEQ ID NO: 158, which corresponds to the sequence of the gene Zeta Chain Of T-Cell Receptor Associated Protein Kinase 70 (Ensembl: ENSG00000115085), e.g. as defined in the NCBI Reference Sequence NM_001079 or the NCBI Reference Sequence NM_207519, in particular the NCBI Reference Sequence of the ZAP70 transcript shown above, and it also relates to the corresponding amino acid sequence, e.g. as set forth in SEQ ID NO: 159 or SEQ ID NO: 160, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_001070 and the NCBI Protein Accession Reference Sequence NP_997402, which encodes a ZAP70 polypeptide.

[0109] The term "ZAP70" also refers to a nucleotide sequence that exhibits high homology to ZAP70, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:157 or SEQ ID NO:158, or an nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:159 or SEQ ID NO:160. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:159 or SEQ ID NO:160, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:157 or SEQ ID NO:158.

[0110] The term "ZBP1" refers to the nucleotide sequence as set forth in SEQ ID NO: 161, SEQ ID NO: 162 or SEQ ID NO: 163, which corresponds to the sequence of the Z-DNA binding protein 1 gene (Ensembl: ENSG00000124256), e.g. the sequence defined in NCBI Reference Sequence NM_030776, NCBI Reference Sequence NM_001160418 or NCBI Reference Sequence NM_001160419, in particular the NCBI Reference Sequence of the ZBP1 transcript shown above, and also to the corresponding amino acid sequence as set forth in SEQ ID NO: 164, SEQ ID NO: 165 or SEQ ID NO: 166, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_110403, NCBI Protein Accession Reference Sequence NP_001153890 and NCBI Protein Accession Reference Sequence NP_001153891, which encodes the ZBP1 polypeptide.

[0111] The term "ZBP1" also refers to a nucleotide sequence that exhibits high homology to ZBP1, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:161, SEQ ID NO:162 or SEQ ID NO:163, or an nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:164, SEQ ID NO:165 or SEQ ID NO:166. The present invention includes a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:164, SEQ ID NO:165 or SEQ ID NO:166, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as set forth in SEQ ID NO:161, SEQ ID NO:162 or SEQ ID NO:163.

[0112] The term "biological sample" or "sample obtained from a subject" refers to any biological material obtained from a subject, e.g., a colorectal cancer patient, via suitable methods known to those skilled in the art. The biological sample used is obtained in a clinically acceptable manner, e.g., in a manner that preserves nucleic acids (especially RNA) or proteins.

[0113] The biological sample(s) include body tissues and / or body fluids, such as, but not limited to, blood [or blood obtained from serum, plasma, or PBMCs (peripheral blood mononuclear cells)], sweat, saliva, urine, and needle or excision biopsies. Furthermore, the biological sample contains cell extracts or cell populations containing epithelial cells, such as cancerous epithelial cells or epithelial cells from tissue suspected to be cancer. The biological sample contains cell populations from glandular tissues, for example, a sample from the colorectum of a subject. In addition, if necessary, cells are purified from the obtained body tissues and body fluids and then used as the biological sample. In some realizations, the sample is a tissue sample, a urine sample, a urine sediment sample, a blood sample, a saliva sample, a semen sample, a sample containing circulating tumor cells, a sample containing extracellular vesicles, exosomes secreted by the prostate, or a cell line or a cancer cell line.

[0114] In one particular realization, a biopsy or resection sample is obtained and / or used. Such a sample may include a cell or a cell lysate.

[0115] It is also conceivable that the contents of the biological sample are subjected to an enrichment step, for example by contacting the sample with a ligand specific for the cell membrane or organelles of a particular cell type, for example colorectal cells, functionalized, for example, with magnetic particles. The material enriched by the magnetic particles is then used for the detection and analysis steps described herein above or below.

[0116] Furthermore, cells, e.g., tumor cells, can also be enriched via a filtration process of a liquid or liquid sample, e.g., blood, urine, etc. Such a filtration process can also be combined with an enrichment step based on ligand-specific interactions as described herein above.

[0117] The term "colorectal cancer" refers to cancer of the colon or rectum. Colorectal cancer begins in either the colon or rectum, which make up the majority of the intestine, part of the gastrointestinal (GI) system. Depending on its site of origin, colorectal cancer may be called colon cancer or rectal cancer. However, these cancers are often classified together as colorectal cancer because of their common characteristics. The term "TNM" refers to a classification system and is used to describe the characteristics of malignant tumors.

[0118] According to the TNM classification system, the term "T stage" refers to the extent and size of a tumor. T stage can have the following attributes: T1 (small localized tumor; generally less than 2 cm in size); T2 (larger localized tumor; generally 2-5 cm in size); T3 (larger locally advanced tumor; generally greater than 5 cm in size); T4 (advanced / metastatic tumor).

[0119] According to the TNM classification system, the term "N stage" refers to the presence and extent of tumor-positive lymph nodes. N stage can have the following attributes: N0 (no tumor-positive lymph nodes present); N1 (tumor-positive lymph nodes present); N2 / N3 (enlarged number of tumor-positive lymph nodes); NX (no evaluation of lymph node status possible).

[0120] According to the TNM classification system, the term "M stage" refers to the presence and extent of tumor metastasis. M stage can have the following attributes: M0 (no distant metastasis present); M1 (distant metastasis present).

[0121] The term "survival" refers to a patient surviving colorectal cancer.

[0122] The expression levels of six or more genes are one or more immune defense response genes, preferably two or more, more preferably three or more, and most preferably all of the immune defense genes; and one or more T cell receptor signaling genes, preferably two or more, more preferably three or more, and most preferably all of the T cell receptor signaling genes; and It is preferred that one or more PDE4D7-related genes, preferably two or more, more preferably three or more, and most preferably all of the PDE4D7-related genes are included.

[0123] In one embodiment: and / or the one or more immune defense response genes include three or more, preferably six or more, more preferably nine or more, and most preferably all of the immune defense genes; and / or the one or more T cell receptor signaling genes include three or more, preferably six or more, more preferably nine or more, and most preferably all of the T cell receptor signaling genes; and / or The one or more PDE4D7-related genes include three or more, preferably six or more, and most preferably all of the PDE4D7-related genes.

[0124] Determining an outcome of the subject with colorectal cancer comprises: combining a first gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all, of the immune defense response genes with a regression function derived from a population of colorectal cancer subjects; and / or combining a second gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all, T cell receptor signaling genes with a regression function derived from a population of colorectal cancer subjects; and / or combining a third gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7 or all, of the PDE4D7-related genes with a regression function derived from a population of colorectal cancer subjects; and / or combining the expression levels of the six or more genes, for example 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more or all of the gene expression levels, with a regression function derived from a population of colorectal cancer subjects; It is preferred that the compound has the formula:

[0125] Cox proportional hazards regression allows the analysis of the impact of multiple risk factors on a tested event, such as survival, in real time. In it, risk factors are binary or discrete variables, such as risk scores or clinical stages, or continuous variables, such as biomarker measurements or gene expression values. The probability of an end point (e.g. death or disease recurrence) is called the hazard. In the regression analysis, next to information about whether the tested end point was reached or not reached, for example by subjects in a patient cohort (e.g. whether the patient died or not), the time to the end point is also taken into account. The hazard is expressed as H(t)=H 0 (t) exp(w 1 ·V 1 +w 2 ·V 2 +w 3 ·V 3 + ), where V 1 , V 2 , V 3 are the predictor variables, and H 0 (t) is the baseline hazard, while H(t) is the hazard at any time t. The hazard rate (i.e., the risk of achieving the event) is Ln[H(t) / H 0 (t)] = w 1 ·V 1 +w 2 ·V 2 +w 3 ·V 3 + , where the coefficients or weights w 1 , w 2 , w 3 ··· is estimated by Cox regression analysis and can be interpreted similarly to logistic regression analysis.

[0126] In one particular realization, a combination of a first gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all, immune defense response genes and a regression function is determined as follows:

number

[0127] In one example, 1 is about -0.8 to 0.2, e.g. -0.32, and w 2 is about 0.0 to 1.0, e.g., 0.4743, and w 3 is about -0.2 to 0.8, e.g., 0.2864, and w 4 is about -1.2 to -0.2, e.g. -0.6683, and w 5 is about -0.9 to 0.1, e.g. -0.3665, and w 6 is about -0.4 to 0.6, e.g., 0.1357, and w 7 is about 0.0 to 1.0, e.g., 0.505, and w 8 is about -0.6 to 0.4, e.g. -0.1024, and w 9 is about 0.2 to 1.2, e.g., 0.7229, and w 10 is about -0.3 to 0.7, e.g., 0.2066, and w 11 is about -0.6 to 0.4, e.g. -0.1209, and w 12 is about -0.8 to 0.2, e.g. -0.2982, and w 13 is about -0.4 to 0.6, for example 0.1005, and w 14 is approximately -0.7 to 0.3, e.g., -0.2253.

[0128] In one particular realization, a combination of a second gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all, T cell receptor signaling genes and a regression function is determined as follows:

number

[0129] In one example, 15 is about -1.2 to 0.2, e.g. -0.72, and w 16 is about 0.0 to 1.0, e.g., 0.5222, and w 17 is approximately -0.1 to 0.9, e.g., 0.4222, and w 18 is about 0.1 to 1.1, e.g., 0.5981, and w 19 is about -0.9 to 0.1, e.g. -0.3978, and w 20 is about 0.0 to 1.0, e.g., 0.5332, and w 21 is about -0.5 to 0.5, e.g., 0.007001, and w 22 is about -0.3 to 0.7, e.g., 0.1881, and w 23 is approximately -0.5 to 0.5, e.g., 0.08063, and w 24 is about -0.7 to 0.3, e.g. -0.2047, and w 25 is about -0.4 to 0.6, e.g., 0.1408, and w 26 is about -0.4 to 0.6, e.g., 0.1038, and w 27 is about -0.6 to 0.4, e.g. -0.1477, and w 28 is about -0.3 to 0.7, e.g., 0.2311, and w 29 is approximately -1.0 to 0.0, e.g. -0.4637, and w 30 is approximately -1.4 to -0.4, e.g. -0.8881, and w31 is approximately -0.3 to 0.7, e.g., 0.1936.

[0130] In one particular realization, a combination of a third gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7 or all, PDE4D7-related genes and a regression function is determined as follows:

number

[0131] In one example, 32 is about -0.4 to 0.6, e.g., 0.09275, and w 33 is about -0.2 to 0.7, e.g., 0.2824, and w 34 is about -0.8 to 0.2, e.g. -0.2594, and w 35 is about -0.5 to 0.5, e.g. -0.0217, and w 36 is about -0.4 to 0.6, e.g., 0.08958, and w 37 is about -0.7 to 0.3, e.g. -0.152, and w 38 is about -0.8 to 0.2, for example -0.2854, and w 39 is approximately -0.6 to 0.4, e.g., -0.1182.

[0132] It is further preferred that the step of determining a prediction of outcome for the colorectal cancer subject further comprises the step of combining the first combination of gene expression profiles, the second combination of gene expression profiles and the third combination of gene expression profiles with a regression function derived from a population of colorectal cancer subjects.

[0133] In one particular realization, the prediction of outcome is determined as follows:

number

[0134] In one example, 40 is about 0.3 to 1.3, e.g., 0.7878, and w 41 is about 0.3 to 1.3, for example 0.7699, and w 42 is about 0.1 to 1.1, for example 0.6176.

[0135] In one particular realization, the combined expression levels of six or more genes are calculated as follows: CRCAI_6 model: (w a [geneA])+(w b [geneB])+(w c [geneC])+(w d [geneD])+(w e [geneE])+(w f [geneF]) Here w x represents the weight of each gene geneX. Exemplary weight values ​​and gene combinations are listed in Tables 3 to 12, but it is within the ability of one of ordinary skill in the art to generate other combinations of six or more genes as defined herein and calculate the respective weights for such models.

[0136] The outcome predictions may also be classified or categorized into one of at least two risk groups based on the value of the outcome prediction. For example, there are two risk groups, three risk groups, four risk groups, or more than four predefined risk groups. Each risk group covers a distinct range (non-overlapping) of values ​​of the outcome prediction. For example, the risk groups represent the probability of occurrence of a particular clinical event, such as 0 to <0.1, 0.1 to <0.25, 0.25 to <0.5, or 0.5 to 1.0.

[0137] It is further preferred that the step of determining a prognosis for the outcome of the colorectal cancer subject is further based on one or more clinical parameters obtained from the colorectal cancer subject.

[0138] As mentioned above, various measurements based on clinical parameters were investigated. It may be possible that predictions could be further improved by further basing the prediction of outcome on such clinical parameters.

[0139] Preferably, the one or more clinical parameters obtained from the colorectal cancer subject comprise one or more of: (i) a T stage attribute (T1, T2, T3 or T4); (ii) an N stage attribute (N0, N1 or N2); and (iii) an M stage attribute (M0, M1). Additionally or alternatively, the clinical parameters comprise one or more other clinical parameters associated with the diagnosis and / or prognosis of colorectal cancer.

[0140] It is further preferred that the step of determining a prediction of outcome for the colorectal cancer subject comprises the step of combining one or more of: (i) a first gene expression profile of one or more immune defense response genes; (ii) a second gene expression profile of one or more T cell receptor signaling genes; (iii) a third gene expression profile of one or more PDE4D7-related genes; and (iv) the combination of the first gene expression profiles, the combination of the second gene expression profiles and the combination of the third gene expression profiles, and one or more clinical parameters obtained from the colorectal cancer subject, with a regression function derived from a population of colorectal cancer subjects.

[0141] In one particular realization, the prediction of outcome is determined as follows:

number

[0142] In one example, 40 is about 0.3 to 1.3, e.g., 0.8098, and w 41 is about 0.2 to 1.2, e.g., 0.7297, and w 42 is about 0.1 to 1.1, e.g., 0.6221, and w 43 is about 0.0 to 1.0, for example 0.4875, and w 44 is about 0.4 to 1.4, e.g., 0.9216.

[0143] Preferably, a biological sample is obtained from said colorectal cancer subject before the start of treatment. The gene expression profile may be determined in the form of mRNA or protein in colorectal cancer tissue. Alternatively, if the gene is present in a soluble form, the gene expression profile may be determined in blood.

[0144] More preferably, the treatment is surgery, radiation therapy, cytotoxic chemotherapy (CTX), short- or long-term chemoradiotherapy (CRT), immunotherapy or any combination thereof.

[0145] The prediction of therapeutic response is preferably unlikely or likely to be associated with efficacy of the treatment, and wherein a treatment is recommended based on said prediction of therapeutic response, and if said prediction of therapeutic response is negative, the recommended treatment comprises one or more of: (i) treatment provided earlier than standard; (ii) radiation therapy with an increased effective dose; (iii) adjuvant treatment such as chemotherapy; (iv) long-term CRT (chemoradiotherapy); and (iv) alternative treatment such as immunotherapy.

[0146] In a further aspect of the present invention there is provided an apparatus for predicting outcome in a subject with colorectal cancer, said apparatus comprising: a first gene expression profile of each of one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all, immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, determined in a biological sample obtained from said colorectal cancer subject; and / or one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all, immune defense response genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70, determined in a biological sample obtained from said colorectal cancer subject; an input for receiving data indicative of a second gene expression profile of each of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes, determined in a biological sample obtained from the colorectal cancer subject; and / or a third gene expression profile of each of one or more selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2, e.g., 1, 2, 3, 4, 5, 6, 7 or all PDE4D7-related genes, determined in a biological sample obtained from the colorectal cancer subject; a processor for determining a prediction of outcome for the subject with colorectal cancer based on the first gene expression profile, or the second gene expression profile, or the third gene expression profile, or the first, second and third gene expression profiles; Optionally, a provider for providing a prediction of outcome for said colorectal cancer subject to a healthcare caregiver or to said colorectal cancer subject. An apparatus is presented comprising:

[0147] According to a further aspect of the present invention there is provided a computer program comprising a plurality of instructions, the plurality of instructions being arranged, when the computer program is run by a computer, to: A first gene expression profile of each of the immune defense response genes, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the immune defense response genes, selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, wherein said first gene expression profile is determined in a biological sample obtained from the colorectal cancer subject, and / or one or more of the immune defense response genes, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the immune defense response genes, selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70, selected from the group consisting of receiving data indicative of a second gene expression profile of each of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes, wherein the second gene expression profile is determined in a biological sample obtained from the subject with colorectal cancer, and / or a third gene expression profile of each of one or more selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2, e.g., 1, 2, 3, 4, 5, 6, 7 or all PDE4D7-related genes, wherein the third gene expression profile is determined in a biological sample obtained from the subject with colorectal cancer; determining a prediction of outcome for the colorectal cancer subject based on the first gene expression profile, or the second gene expression profile, or the third gene expression profile, or the first, second and third gene expression profiles; Optionally, providing a prediction of outcome for said colorectal cancer subject to a health care provider or to said colorectal cancer subject; A computer program is presented, which when executed by a computer executes a method comprising:

[0148] In a further aspect of the invention, at least one primer and / or probe for determining expression levels of six or more genes selected from a first gene expression profile, a second gene expression profile, and a third gene expression profile in a biological sample obtained from the colorectal cancer subject, wherein the six or more gene expression levels include at least one or more gene expression levels selected from the third gene expression profile and at least one or more gene expression levels selected from the first and / or second gene expression profiles; the first gene expression profile consists of immune defense response genes selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; the second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; the third gene expression profile consists of PDE4D7-related genes selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2; At least one primer and / or probe; Optionally, an apparatus as claimed in claim 11 or a computer program as claimed in claim 12; A diagnostic kit is provided comprising: Or, at least one primer and / or probe for determining a first gene expression profile of each of one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1 in a biological sample obtained from the subject; and / or at least one primer and / or probe for determining a second gene expression profile for each of one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70 in a biological sample obtained from the subject; and / or At least one primer and / or probe for determining the gene expression profile of one or more, for example 1, 2, 3, 4, 5, 6, 7 or all, of the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2 in a biological sample obtained from a subject; Optionally, an apparatus as claimed in claim 11 or a computer program as claimed in claim 12. A diagnostic kit comprising:

[0149] In a further aspect of the present invention, the use of a diagnostic kit according to claim 13 is provided.

[0150] The use of the diagnostic kit according to claim 14 is preferably in a method for predicting the outcome of a subject with colorectal cancer.

[0151] In a further aspect of the invention, receiving one or more biological samples obtained from a subject with colorectal cancer; using the kit of claim 13 to determine expression levels of six or more genes selected from a first gene expression profile, a second gene expression profile, and a third gene expression profile, wherein the six or more gene expression levels include at least one or more gene expression levels selected from the third gene expression profile and at least one or more gene expression levels selected from the first and / or second gene expression profile; the first gene expression profile consists of immune defense response genes selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; the second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; the third gene expression profile consists of PDE4D7-related genes selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2; Alternatively, a method is presented comprising the steps of: receiving one or more biological samples obtained from a subject with colorectal cancer; and / or using the kit of claim 13 to determine a first gene expression profile of each of one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1 in a biological sample obtained from the subject; and / or and / or using the kit of claim 13 to determine a second gene expression profile of each of one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70 in a biological sample obtained from the subject; and / or using the kit of claim 13 to determine a third gene expression profile of each of one or more, e.g., one, two, three, four, five, six, seven or all, PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2 in a biological sample obtained from the subject; A method is presented that includes:

[0152] In a further aspect of the invention, determining a prediction of outcome for the subject with colorectal cancer based on expression levels of six or more genes; Optionally, providing a prediction of outcome for said colorectal cancer subject to a health care provider or to said colorectal cancer subject. 2. Use of expression levels of six or more genes selected from a first gene expression profile, a second gene expression profile and a third gene expression profile in a method of predicting outcome of a subject with colorectal cancer, wherein the six or more gene expression levels comprise at least one or more gene expression levels selected from the third gene expression profile and at least one or more gene expression levels selected from the first and / or second gene expression profile, and wherein the first gene expression profile comprises: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRF The present invention provides a method for treating a pulmonary artery disease comprising administering to a patient a therapeutically effective amount of ... determining a prediction of outcome for the subject with colorectal cancer based on the first gene expression profile, or the second gene expression profile, or the third gene expression profile, or the first, second and third gene expression profiles; Optionally, providing a healthcare caregiver or to the colorectal cancer subject, a prediction or personalization of an outcome for the colorectal cancer subject, or a treatment recommendation based on the prediction or personalization of an outcome for the colorectal cancer subject; a first gene expression profile of one or more, for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and / or a first gene expression profile of one or more, for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the immune defense response genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PR The present invention relates to use of a second gene expression profile of one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all, T cell receptor signaling genes selected from the group consisting of KACA, PRKACB, PTPRC and ZAP70, and / or a third gene expression profile of one or more, such as 1, 2, 3, 4, 5, 6, 7 or all, PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2.

[0153] The method according to claim 1, the device according to claim 11, the computer program according to claim 12, the diagnostic kit according to claim 13, the use of the diagnostic kit according to claim 14, the method according to claim 16 and the use of the first, second and / or third gene expression profile according to claim 17 shall be understood to have similar and / or identical preferred embodiments, in particular as defined in the dependent claims.

[0154] It shall be understood that a preferred embodiment of the invention can also be any combination of the dependent claims or the above embodiments with the respective independent claim.

[0155] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter. [Brief description of the drawings]

[0156] [Figure 1] FIG. 1 shows a schematic and representative flow chart of an embodiment of a method for predicting outcome in a subject with colorectal cancer. [Diagram 2] FIG. 1 shows Kaplan-Meier curves of the IDR_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the IDR_model). [Diagram 3] Figure 1 shows the Kaplan-Meier curves of the IDR_model in the TCGA cohort of 83 colorectal cancer patients (the test set used to validate the IDR_model developed on the 168 patient training set). [Figure 4] FIG. 1 shows Kaplan-Meier curves of the TCR_SIGNALING_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the TCR_SIGNALING_model). [Diagram 5] Figure 1 shows Kaplan-Meier curves of the TCR_SIGNALING_model in the TCGA cohort of 83 colorectal cancer patients (test set used to validate the TCR_SIGNALING_model developed on a 168 patient training set). [Figure 6] FIG. 1 shows Kaplan-Meier curves for the PDE4D7_CORR_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the PDE4D7_CORR_model). [Figure 7] FIG. 1 shows Kaplan-Meier curves of the PDE4D7_CORR_model in the TCGA cohort of 83 colorectal cancer patients (test set used to validate the PDE4D7_CORR_model developed on the 168 patient training set). [Figure 8] Figure 1 shows Kaplan-Meier curves of the CRCAI_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_model). [Figure 9]Figure 1 shows Kaplan-Meier curves of the CRCAI_model in the TCGA cohort of 83 colorectal cancer patients (the test set used to validate the CRCAI_model developed on the 168 patient training set). [Figure 10] Figure 1 shows Kaplan-Meier curves of the CRCAI&Clinical_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI&Clinical_model). [Figure 11] Figure 1 shows Kaplan-Meier curves of the CRCAI&Clinical_model in the TCGA cohort of 83 colorectal cancer patients (the test set used to validate the CRCAI&Clinical_model developed on the 168 patient training set). [Figure 12] Kaplan-Meier curves for CRCAI_model in colorectal cancer cohort from the GSE41248 dataset. The clinical endpoint tested was overall death. [Figure 13] Figure 1 shows Kaplan-Meier curves for the CRCAI_model in the colorectal cancer cohort from the GSE41248 dataset. The clinical endpoint tested was cancer-specific death. [Figure 14] Kaplan-Meier curves for CRCAI&Clinical_model in colorectal cancer cohort from GSE41248 dataset. Clinical endpoints tested were overall death. [Figure 15] Kaplan-Meier curves of CRCAI&Clinical_model in colorectal cancer cohort from GSE41248 dataset. The clinical endpoint tested was cancer-specific death. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0157] Summary of outcome prediction FIG. 1 shows, diagrammatically and exemplarily, a flow chart of an embodiment of a method for predicting outcome in a subject with colorectal cancer.

[0158] The method begins in step S100.

[0159] In step S102, a biological sample is obtained from each of a first set of patients (subjects) diagnosed with colorectal cancer. Preferably, colorectal cancer monitoring is performed on these colorectal cancer patients for a period of time, such as at least 1 year, at least 2 years, or about 5 years, after obtaining the biological sample.

[0160] In step S104, a first gene expression profile of one or more, for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and / or a first gene expression profile of one or more, for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all immune defense response genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD4E, CD5E, CD6E, CD7E, CD8E, CD9E, CD10E, CD11E, CD12E, CD13E, CD14E, CD15E, CD16E, CD17E, CD18E, CD19E, CD20E, CD21E, CD22E, CD23E, CD247, CD25E, CD26E, CD27E, CD28E, CD3E, CD4E, CD5E, CD6E, CD7E, CD8E, CD9E, CD10E, CD11E, CD12E, CD13E, CD14E, CD15E, CD16E, CD17E, CD18E, CD19E, CD247, CD28, CD3E, CD4E, CD19E, CD25E, CD18E, CD19E, CD26E, CD19E, CD27E, CD19E, CD28E, CD3E, CD4E, CD19E, CD28E, CD3E, CD4E A second gene expression profile of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all of the T cell receptor signaling genes selected from the group consisting of 3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70, and / or a third gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7 or all of the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2, is obtained. A typical gene expression profile includes expression levels (e.g., values) of each of two or more genes, and may be normalized using the respective values ​​of a set of reference genes, such as B2M, HPRT1, POLR2A, and / or PUM1. In one realization, the gene expression levels of each of the two or more genes in the first gene expression profile and / or the second gene expression profile and / or the third gene expression profile are normalized to one or more reference genes selected from the group consisting of ACTB, ALAS1, B2M, HPRT1, POLR2A, PUM1, RPLP0, TBP, TUBA1B, and / or YWHAZ, such as at least one, or at least two, or at least three, preferably all, of these reference genes.

[0161] In step S106, a first gene expression profile of two or more immune defense response genes, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and / or ZBP1, and / or two or more T cell receptor signaling genes, CD2, CD247, CD28, CD3E, CD3G, C, CD40, C, CD41, C, CD42, C, CD43, C, CD44, C, CD45, C, CD46, C, CD47, C, CD48, C, CD49, C, CD50, C, CD51, C, CD52, C, CD53, C, CD54, C, CD55, C, CD56, C, CD57, C, CD58, C, CD59, C, CD60, C, CD61, C, CD62, C, CD63, C, CD64, C, CD65, C, CD66, C, CD67, C, CD68, C, CD69, C, CD70, C, CD71, C, CD72, C, CD73, C, CD74, C, CD75, C, CD76, C, CD77, C, CD78, C, CD79, C, CD80, C, CD81, C, CD82, C, CD83, C, CD84, C, CD85, C, CD86, C, CD87, C, CD88, C, CD89, C, CD90, C, CD91, C, CD92, C, CD93, C, CD94, C, CD95, C, CD96, C, CD97, C, CD98, C, CD99, C, CD99, C, CD100, C, CD101, C, CD10 Based on the second gene expression profile of D4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC and / or ZAP70, and / or the third gene expression profile of two or more PDE4D7-related genes, ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and / or VWA2, and the respective results obtained from monitoring, a regression function is determined to give a prediction of outcome.In a particular realization, the regression function is determined as specified in formula (4) above.

[0162] In step S108, a biological sample is obtained from a patient (subject, i.e., individual), which may be a new patient or a first set of patients.

[0163] In step S110, a first gene expression profile of one or more, for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and / or a first gene expression profile of one or more, for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of immune defense response genes selected from the group consisting of CD2, CD247, CD28, CD3E, A second gene expression profile of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all, T cell receptor signaling genes selected from the group consisting of CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70, and / or a third gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7 or all, PDE4D7-related genes, is obtained. In one realization, the gene expression levels of each of two or more genes in the first gene expression profile and / or the second gene expression profile and / or the third gene expression profile are normalized to one or more reference genes selected from the group consisting of ACTB, ALAS1, B2M, HPRT1, POLR2A, PUM1, RPLP0, TBP, TUBA1B and / or YWHAZ, such as at least one, or at least two, or at least three, preferably all, of these reference genes, which is substantially the same as step S104.

[0164] In step S112, a prediction of outcome based on the first, second, and third gene expression profiles is determined for the patient using the regression function, as will be described in more detail later in this description.

[0165] In step S114, a treatment recommendation is provided, for example, to the patient or his or her guardian, to a physician, or to another health care professional, based on the prediction or personalization. For this purpose, the prediction or personalization may be categorized into one of a set of predefined risk groups based on the value of the prediction. In one particular realization, the treatment is a radiation treatment, and the prediction of the radiation treatment response is unlikely or likely for the effectiveness of the radiation treatment. If the prediction of the radiation treatment response is unlikely, the recommended treatment includes one or more of: (i) a treatment provided earlier than standard; (ii) a radiation treatment with an increased effective dose; (iii) an adjuvant treatment such as chemotherapy; (iv) a long-term CRT (chemoradiotherapy), and; (iv) an alternative treatment such as immunotherapy.

[0166] In step S116, the method ends.

[0167] In one embodiment, the gene expression profile in steps S104 and S110 is determined by detecting expression of mRNA using two or more primers and / or probes and / or two or more sets thereof.

[0168] In one embodiment, steps S104 and S110 further comprise obtaining clinical parameters from the first set of patients and the patients, respectively. The clinical parameters comprise one or more of: (i) a T stage attribute (T1, T2, T3 or T4); (ii) an N stage attribute (N0, N1 or N2); and (iii) an M stage attribute (M0, M1). Additionally or alternatively, the clinical parameters comprise one or more other clinical parameters related to the diagnosis and / or prognosis of colorectal cancer. The regression function for providing a prediction of outcome determined in step S106 is then further based on the one or more clinical parameters obtained from at least a portion of the first set of patients. Then, in step S112, a prediction of outcome is determined for the patient using the regression function further based on the one or more clinical parameters, e.g., the N stage attribute, obtained from the patient. In one particular realization, the regression function is determined as defined in formula (5) above.

[0169] Based on significant correlation with post-treatment survival outcomes, the identified molecules are expected to provide predictive value for the efficacy of primary colorectal cancer treatment.

[0170] result For each gene, the log2 expression value obtained in the download from the TCGA database (TCGA Colorectal Adenocarcinoma, Firehose legacy, accessed on March 6, 2020, http: / / linkedomics.org / login.php) was obtained.

[0171] The log2 expression value for each gene was calculated as

number

[0172] This process results in the transformed log2_gene values ​​being distributed around a mean of 0 with a standard deviation of 1.

[0173] For multivariate analysis of genes of interest, the log2_gene transformed z-score values ​​of each gene were used as input.

[0174] Cox regression analysis We then set out to test whether the combination of 14 immune defense response genes, 17 T cell receptor signaling genes, 8 PDE4D7-related genes, and their combinations showed predictive value for colorectal cancer.Using Cox regression, the expression levels of the 14 immune defense response genes, 17 T cell receptor signaling genes, and 8 PDE4D7-related genes, respectively, were modeled against overall survival in the TCGA cohort of 377 colorectal cancer patients.

[0175] From the TCGA colorectal cancer cohort, only samples for which clinical parameters, gene expression values, and survival information were combined were included. From this subset, only samples from patients with non-metastatic disease (m0) were included, resulting in a total number of 251 patients. These 251 patients were randomly divided into 3 groups. Cohort 1 (n=168) was used as the training cohort and consisted of groups 1+2. Cohort 2 (n=83) consisted of group 3 and was used to validate the risk model obtained from the training cohort.

[0176] The Cox regression function was derived as follows:

number

[0177] Weight w 1 ~w 39 Details about the above are shown in Table 1 below.

[0178] [Table 1] TIFF2024502489000010.tif110170

[0179] Based on three individual Cox regression models (IDR_model, TCR_SIGNALING_model, PDE4D7_CORR_model), the combinations for overall survival were then modeled again using Cox regression with (CRCAI_model) or without (CRCAI&Clinical_model) clinical variables (N stage attribute) in each cohort of colorectal cancer patients. Two models were tested in the Kaplan-Meier survival analysis.

[0180] The Cox regression function was derived as follows:

number

[0181] Weight w 40 ~w 44 Details about this are shown in Table 2 below.

[0182] [Table 2]

[0183] Kaplan-Meier survival analysis For Kaplan-Meier survival curve analysis, the Cox functions of the risk models (IDR_model, TCR_SIGNALING_model, PDE4D7_CORR_model, CRCAI_model, and CRCAI&Clinical_model) were categorized into two subcohorts based on cutoff values. The thresholds for separating the groups into low and high risk were based on the risk of experiencing the clinical endpoint (outcome) predicted by the respective Cox regression models.

[0184] Figure 2 shows the Kaplan-Meier curves of the IDR_model in the TCGA cohort of 168 colorectal cancer patients (training set used to develop the IDR_model). The clinical endpoint tested was death overall (log-rank p=0.009; HR=2.5; 95% CI=1.3-5.0). The supplementary list below represents the number of risk patients in the IDR_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown: low risk: 80, 45, 21, 15, 9, 6, 4, 1, 0; high risk: 88, 52, 29, 14, 9, 4, 3, 0, 0.

[0185] Figure 3 shows the Kaplan-Meier curves of the IDR_model in the TCGA cohort of 83 colorectal cancer patients (test set used to validate the IDR_model developed on the 168 patient training set). The clinical endpoint tested was death overall (log-rank p=0.8; HR=0.9; 95% CI=0.3-2.6). The supplementary list below represents the number of risk patients in the IDR_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown: low risk: 42, 29, 8, 4, 4, 2, 2, 1, 0; high risk: 41, 19, 9, 4, 2, 0, 0, 0, 0.

[0186] Figure 4 shows the Kaplan-Meier curves of the TCR_SIGNALING_model in the TCGA cohort of 168 colorectal cancer patients (training set used to develop the TCR_SIGNALING_model). The clinical endpoint tested was death overall (log-rank p=0.003; HR=2.9; 95% CI=1.4-5.9). The supplementary list below represents the number of risk patients in the TCR_SIGNALING_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown: low risk: 81, 46, 23, 10, 4, 3, 1, 0, 0; high risk: 87, 51, 27, 19, 14, 7, 6, 1, 0.

[0187] Figure 5 shows the Kaplan-Meier curves of the TCR_SIGNALING_model in the TCGA cohort of 83 colorectal cancer patients (test set used to validate the TCR_SIGNALING_model developed on the 168 patient training set). The clinical endpoint tested was death overall (log-rank p=0.02; HR=3.7; 95% CI=1.3-10.9). The supplementary list below represents the number of risk patients in the TCR_SIGNALING_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown: low risk: 46, 26, 10, 6, 5, 2, 2, 1, 0; high risk: 37, 22, 7, 2, 1, 0, 0, 0, 0.

[0188] Figure 6 shows the Kaplan-Meier curves of the PDE4D7_CORR_model in the TCGA cohort of 95 colorectal cancer patients (training set used to develop the PDE4D7_CORR_model). The clinical endpoint tested was death overall (log-rank p<0.0001; HR=10.7; 95% CI=3.5-32.4). The supplementary list below represents the number of risk patients in the PDE4D7_CORR_model classes analyzed (threshold=0). That is, risk patients are shown for all +20 months period: low risk: 143, 86, 45, 27, 17, 10, 7, 1, 0; high risk: 25, 11, 5, 2, 1, 0, 0, 0, 0.

[0189] Figure 7 shows the Kaplan-Meier curves of the PDE4D7_CORR_model in the TCGA cohort of 83 colorectal cancer patients (test set used to validate the PDE4D7_CORR_model developed on the 168 patient training set). The clinical endpoint tested was death overall (log-rank p<0.8; HR=1.2; 95% CI=0.2-5.9). The supplementary list below represents the number of risk patients in the PDE4D7_CORR_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown: low risk: 72, 42, 15, 7, 6, 2, 2, 1, 0; high risk: 11, 6, 2, 1, 0, 0, 0, 0, 0.

[0190] Figure 8 shows the Kaplan-Meier curves of the CRCAI_model in the TCGA cohort of 168 colorectal cancer patients (training set used to develop the CRCAI_model). The clinical endpoint tested was death overall (log-rank p=0.0001; HR=4.6; 95% CI=2.2-9.7). The supplementary list below represents the number of risk patients in the CRCAI_model classes analyzed (threshold=0.5). That is, risk patients in all +20 months periods are shown: low risk: 117, 69, 37, 19, 9, 6, 3, 0, 0; high risk: 51, 28, 13, 10, 9, 4, 4, 1, 0.

[0191] Figure 9 shows the Kaplan-Meier curves of the CRCAI_model in the TCGA cohort of 83 colorectal cancer patients (test set used to validate the CRCAI_model developed on the 168 patient training set). The clinical endpoint tested was death overall (log-rank p=0.005; HR=5.9; 95% CI=1.7-20.2). The supplementary list below represents the number of risk patients in the analyzed CRCAI_model classes (threshold=0.5). That is, risk patients in all +20 months periods are shown: low risk: 66, 39, 10, 6, 5, 2, 2, 1, 0; high risk: 17, 9, 7, 2, 1, 0, 0, 0, 0.

[0192] Figure 10 shows the Kaplan-Meier curves of the CRCAI&Clinical_model in the TCGA cohort of 168 colorectal cancer patients (training set used to develop the CRCAI&Clinical_model). The clinical endpoint tested was death overall (log-rank p=0.0001; HR=4.0; 95% CI=2.0-8.0). The supplementary list below represents the number of risk patients in the analyzed CRCAI&Clinical_model classes (threshold=0.5). That is, risk patients in all +20 months are shown: low risk: 105, 59, 29, 15, 7, 5, 3, 0, 0; high risk: 63, 38, 21, 14, 11, 5, 4, 1, 0.

[0193] Figure 11 shows the Kaplan-Meier curves of the CRCAI&Clinical_model in the TCGA cohort of 83 colorectal cancer patients (the test set used to validate the CRCAI&Clinical_model developed on the 168 patient training set). The clinical endpoint tested was death overall (log-rank p=0.01; HR=5.5; 95% CI=1.4-21.8). The supplementary list below represents the number of risk patients in the analyzed CRCAI&Clinical_model classes (threshold=0.5). That is, risk patients in all +20 months periods are shown: low risk: 62, 40, 14, 7, 6, 2, 2, 1, 0; high risk: 21, 8, 3, 1, 0, 0, 0, 0, 0.

[0194] Below we present some additional results for the colorectal cancer cohort from the GSE41248 (www.ncbi.nlm.nih.gov / geo) dataset:

[0195] Figure 12 shows the Kaplan-Meier curves of the CRCAI_model in the colorectal cancer cohort from the GSE41248 dataset. The model was trained on 2 / 3 of the samples from patients with non-metastatic (m0) and metastatic (m1) disease, and then tested on the remaining 1 / 3 of the m0 and m1 samples. This trained model was then tested only on samples from patients with non-metastatic disease (m0) to provide the same situation as the TCGA colorectal cancer cohort. The clinical endpoint tested was death overall (log-rank p=0.001; HR=3.2; 95% CI=1.6-6.5). The supplementary list below represents the number of patients at risk for the CRCAI_model classes analyzed (threshold=0.5). That is, for all +20 months, risk patients are shown: low risk: 132, 123, 113, 97, 75, 50, 32, 14, 6, 3, 2, 0; high risk: 31, 27, 23, 22, 12, 8, 4, 0, 0, 0, 0, 0.

[0196] Figure 13 shows the Kaplan-Meier curves of the CRCAI_model in the colorectal cancer cohort from the GSE41248 dataset. The model was trained on 2 / 3 of the samples from patients with non-metastatic (m0) and metastatic (m1) disease and then tested on the remaining 1 / 3 of the m0 and m1 samples. This trained model was then tested only on samples from patients with non-metastatic disease (m0) to make the situation the same as the TCGA colorectal cancer cohort. The clinical endpoint tested was cancer-specific death (log-rank p<0.0001; HR=12.9; 95% CI=3.9-43.0). The supplementary list below represents the number of patients at risk for the CRCAI_model classes analyzed (threshold=0.5). That is, for all +20 months, risk patients are shown: low risk: 94, 86, 80, 67, 51, 34, 18, 8, 3, 2, 2, 0; high risk: 23, 19, 16, 15, 7, 4, 3, 0, 0, 0, 0, 0.

[0197] Figure 14 shows the Kaplan-Meier curves of the CRCAI&Clinical_model in the colorectal cancer cohort from the GSE41248 dataset. The model was trained on 2 / 3 of the samples from patients with non-metastatic (m0) and metastatic (m1) disease, and then tested on the remaining 1 / 3 of the m0 and m1 samples. This trained model was then tested only on samples from patients with non-metastatic disease (m0) to make the situation the same as the TCGA colorectal cancer cohort. The clinical endpoint tested was death overall (log-rank p=0.0002; HR=3.3; 95% CI=1.8-6.1). The supplementary list below represents the number of patients at risk for the CRCAI&Clinical_model classes analyzed (threshold=0.5). That is, for all +20 months, risk patients are shown: low risk: 120, 111, 103, 90, 71, 45, 30, 14, 6, 3, 2, 0; high risk: 43, 39, 33, 29, 16, 13, 6, 0, 0, 0, 0, 0.

[0198] Figure 15 shows the Kaplan-Meier curves of the CRCAI&Clinical_model in the colorectal cancer cohort from the GSE41248 dataset. The model was trained on 2 / 3 of the samples from patients with non-metastatic (m0) and metastatic (m1) disease, and then tested on the remaining 1 / 3 of the m0 and m1 samples. This trained model was then tested only on samples from patients with non-metastatic disease (m0) to make the situation the same as the TCGA colorectal cancer cohort. The clinical endpoint tested was cancer-specific death (log-rank p=0.0001; HR=16.8; 95% CI=6.0-47.1). The supplementary list below represents the number of patients at risk for the CRCAI&Clinical_model classes analyzed (threshold=0.5). That is, for all +20 months, risk patients are shown: low risk: 84, 76, 71, 61, 48, 31, 17, 8, 3, 2, 2, 0; high risk: 33, 29, 25, 21, 10, 7, 4, 0, 0, 0, 0, 0.

[0199] The analysis of the Kaplan-Meier survival curves shown in Figures 2 to 15 demonstrates the existence of different patient risk groups. The patient's risk group is determined by the probability of suffering the individual clinical endpoint (overall death) calculated by the respective risk model shown in the figure. Depending on the prediction of the patient's risk of dying from colorectal cancer (i.e. depending on which risk group the patient belongs to), different types of therapeutic interventions are indicated. In the low-risk group (probability less than 0.5), the standard of care (SOC) allows an acceptable long-term oncological control. This patient is clearly not a case of the patient group with a risk of more than 0.5 of experiencing any of the relevant outcomes. In the patient group with a risk of more than 0.5, the therapeutic intervention or the extension of the application of alternative treatments is necessary. Alternative options for treatment extension are adjuvant treatment with radiation or cytotoxic drugs, or alternative treatments such as immunotherapy (e.g. atezolizumab; pembrolizumab; nivolumab; avelumab; durvalumab) or other experimental treatments.

[0200] We next investigated whether a smaller subset of genes still had predictive power. To investigate this, we generated 10 groups of 6 randomly selected genes that met the following criteria: At least one gene was selected from the PDE4D7-related gene set. The following selections were obtained:

[0201] [Table 3]

[0202] [Table 4]

[0203] [Table 5]

[0204] [Table 6]

[0205] [Table 7]

[0206] [Table 8]

[0207] [Table 9]

[0208] [Table 10]

[0209] [Table 11]

[0210] [Table 12]

[0211] In this section, we present additional results for Cox regression models based on multiple gene models of both colorectal cancer, including randomly selected combinations of the six genes listed in Tables 3-12 above. The variables and corresponding weights are presented above. The Cox regression models are plotted in Kaplan-Meier curve analyses in Figures 16-25.

[0212] For Kaplan-Meier curve analysis, the Cox regression functions of the 10 risk models (CRCAI6.1 to CRCAI6.10) shown above were categorized into two subcohorts (low risk vs. high risk) based on cutoff values. The thresholds for separating the groups into low and high risk were based on the risk of experiencing the clinical endpoint (outcome) predicted by each Cox regression model.

[0213] Figure 16 shows the Kaplan-Meier curves of the CRCAI_6.1_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_6.1_model). The clinical endpoint tested was death overall (log-rank p=0.03; HR=2.2; 95% CI=1.1-4.5). The supplementary list below the graph shows the number of patients at risk for the CRCAI_6.1_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown. The upper line in the graph illustrates patients below or equal to the threshold, and the lower line in the graph illustrates patients above the threshold.

[0214] Figure 17 shows the Kaplan-Meier curves of the CRCAI_6.2_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_6.2_model). The clinical endpoint tested was death overall (log-rank p=0.01; HR=2.4; 95% CI=1.9-4.7). The supplementary list below the graph shows the number of patients at risk for the CRCAI_6.2_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown. The upper line in the graph illustrates patients below or equal to the threshold, and the lower line in the graph illustrates patients above the threshold.

[0215] Figure 18 shows the Kaplan-Meier curves of the CRCAI_6.3_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_6.3_model). The clinical endpoint tested was death overall (log-rank p=0.02; HR=2.4; 95% CI=1.1-5.1). The supplementary list below the graph shows the number of patients at risk for the CRCAI_6.3_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown. The upper line in the graph illustrates patients below or equal to the threshold, and the lower line in the graph illustrates patients above the threshold.

[0216] Figure 19 shows the Kaplan-Meier curves of the CRCAI_6.4_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_6.4_model). The clinical endpoint tested was death overall (log-rank p=0.002; HR=3.0; 95% CI=1.5-5.9). The supplementary list below the graph shows the number of patients at risk for the CRCAI_6.4_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown. The upper line in the graph illustrates patients below or equal to the threshold, and the lower line in the graph illustrates patients above the threshold.

[0217] Figure 20 shows the Kaplan-Meier curves of the CRCAI_6.5_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_6.5_model). The clinical endpoint tested was death overall (log-rank p=0.007; HR=2.6; 95% CI=1.3-5.1). The supplementary list below the graph shows the number of patients at risk for the CRCAI_6.5_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown. The upper line in the graph illustrates patients below or equal to the threshold, and the lower line in the graph illustrates patients above the threshold.

[0218] Figure 21 shows the Kaplan-Meier curves of the CRCAI_6.6_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_6.6_model). The clinical endpoint tested was death overall (log-rank p=0.001; HR=3.4; 95% CI=1.6-7.2). The supplementary list below the graph shows the number of patients at risk for the CRCAI_6.6_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown. The upper line in the graph illustrates patients below or equal to the threshold, and the lower line in the graph illustrates patients above the threshold.

[0219] Figure 22 shows the Kaplan-Meier curves of the CRCAI_6.7_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_6.7_model). The clinical endpoint tested was death overall (log-rank p=0.03; HR=2.1; 95% CI=1.1-4.2). The supplementary list below the graph shows the number of patients at risk for the CRCAI_6.7_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown. The upper line in the graph illustrates patients below or equal to the threshold, and the lower line in the graph illustrates patients above the threshold.

[0220] Figure 23 shows the Kaplan-Meier curves of the CRCAI_6.8_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_6.8_model). The clinical endpoint tested was death overall (log-rank p=0.004; HR=2.8; 95% CI=1.4-5.5). The supplementary list below the graph shows the number of patients at risk for the CRCAI_6.8_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown. The upper line in the graph illustrates patients below or equal to the threshold, and the lower line in the graph illustrates patients above the threshold.

[0221] Figure 24 shows the Kaplan-Meier curves of the CRCAI_6.9_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_6.9_model). The clinical endpoint tested was death overall (log-rank p=0.01; HR=2.5; 95% CI=1.2-5.1). The supplementary list below the graph shows the number of patients at risk for the CRCAI_6.9_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown. The upper line in the graph illustrates patients below or equal to the threshold, and the lower line in the graph illustrates patients above the threshold.

[0222] Figure 25 shows the Kaplan-Meier curves of the CRCAI_6.10_model in the TCGA cohort of 168 colorectal cancer patients (the training set used to develop the CRCAI_6.10_model). The clinical endpoint tested was death overall (log-rank p=0.004; HR=3.0; 95% CI=1.4-6.2). The supplementary list below the graph shows the number of patients at risk for the CRCAI_6.10_model classes analyzed (threshold=0). That is, risk patients in all +20 months periods are shown. The upper line in the graph illustrates patients below or equal to the threshold, and the lower line in the graph illustrates patients above the threshold.

[0223] From these data, it can be concluded that the models of six genes selected from the immune defense response signature, T cell signaling signature and PDE4D7-related signature described herein are sufficient to make predictions, considering that at least one gene out of the six genes is selected from the PDE4D7-related gene signature.It is expected that these results can be extrapolated to randomly select more than six genes, since a set of ten randomly selected six genes that meet these criteria can significantly stratify patient risk.

[0224] Consideration The efficacy of treatment for colorectal cancer is limited, especially for patients with high risk of disease recurrence after primary intervention, which leads to disease progression and ultimately patient death. Prediction of treatment outcome is very challenging, as many factors are involved in treatment efficacy and disease recurrence. Important factors may not yet be identified, and the influence of other factors cannot be precisely determined. Several clinicopathological measurements are currently being investigated and applied in clinical practice to improve response prediction and treatment selection, which has brought about some improvements. Nevertheless, better prediction of treatment response is still strongly needed to increase the success rate of colorectal cancer treatment.

[0225] Molecules have been identified whose expression has been shown to be significantly associated with mortality after first-line treatment for colorectal cancer and therefore is expected to improve prediction of efficacy of second-line treatments. This can be achieved by 1) directing patients at low risk of progressive disease, which correlates with cancer death, to standard treatments, and / or 2) directing patients at high risk of progressive disease and subsequent cancer death to alternative, potentially more effective forms of treatment compared to the currently applied standard treatments. This will reduce patient suffering by sparing ineffective treatments and reduce costs otherwise spent on ineffective treatments.

[0226] Those skilled in the art can understand and effect other variations to the disclosed implementations in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0227] In the claims, the words "comprise" and "comprise" do not exclude other elements or steps and the singular does not exclude a plurality.

[0228] One or more steps of the method illustrated in Fig. 1 are implemented in a computer program product running on a computer. The computer program product includes a non-transitory computer readable recording medium, such as a disk, a hard drive, etc., on which a control program is recorded (stored). Common forms of non-transitory computer readable media include, for example, a floppy disk, a flexible disk, a hard disk, a magnetic tape or other magnetic storage medium, a CD-ROM, a DVD or other optical medium, a RAM, a PROM, an EPROM, a FLASH-EPROM or other memory chip or cartridge, or other non-transitory medium that can be read and used by a computer.

[0229] Alternatively, one or more steps of the method may be implemented in a transitory medium, such as a transmittable carrier wave in which the control program is embodied as a data signal, using a transmission medium such as sound or light waves, such as those generated during radio wave and infrared data communications.

[0230] Exemplary methods are implemented on one or more general purpose computers, special purpose computers, programmed microprocessors or microcontrollers and peripheral integrated circuit elements, hardwired electronic or logic circuits such as ASICs or other integrated circuits, digital signal processors, discrete element circuits, programmable logic devices such as PLDs, PLAs, FPGAs, Graphical cards CPUs (GPUs) or PALs, etc. In general, any device capable of implementing a finite machine capable of sequentially implementing the flow chart shown in FIG. 1 can be used to implement one or more steps of the exemplary method of risk stratification for treatment selection in patients with prostate cancer. As will be appreciated, the steps of the method may all be computer-implemented, although in some embodiments, one or more steps are performed at least partially manually.

[0231] A computer-implemented process may also be created by loading computer program instructions on a computer, other programmable data processing apparatus, or other device to cause a series of work steps to be executed on the computer, other programmable apparatus, or other device such that the instructions running on the computer or other programmable apparatus provide a process for performing the functions / operations specified herein.

[0232] Any reference signs in the claims should not be construed as limiting the scope.

[0233] The present invention relates to a method for predicting outcome in a subject with colorectal cancer, said method comprising: determining or receiving results of the determination of expression levels of six or more genes selected from a first gene expression profile, a second gene expression profile, and a third gene expression profile, wherein the six or more gene expression levels include at least one or more gene expression levels selected from the third gene expression profile and at least one or more gene expression levels selected from the first and / or second gene expression profiles; the first gene expression profile consists of immune defense response genes selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; the second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; the third gene expression profile consists of PDE4D7-related genes selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2; the first, second and third gene expression profiles are determined in a biological sample obtained from the subject with colorectal cancer; determining a prognosis of outcome for the colorectal cancer subject based on expression levels of six or more genes; Optionally, providing a prediction of outcome for said colorectal cancer subject to a health care provider or to said colorectal cancer subject. In one embodiment, the six or more gene expression levels include one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all immune defense response genes, and one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all T cell receptor signaling genes, and one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all PDE4D7 related genes.

[0234] Alternatively, the present invention relates to a method of predicting outcome in a subject with colorectal cancer, the method comprising the steps of determining, or receiving the result of determining, a first gene expression profile for each of one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all, immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and / or a second gene expression profile of each of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70. determining or receiving a result of the determination of a gene expression profile, wherein the second gene expression profile is determined in a biological sample obtained from the colorectal cancer subject; and / or determining or receiving a result of the determination of a third gene expression profile for each of one or more, such as one, two, three, four, five, six, seven or all, PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2, wherein the third gene expression profile is determined in a biological sample obtained from the colorectal cancer subject; determining a prediction of outcome for the colorectal cancer subject based on the first gene expression profile, or the second gene expression profile, or the third gene expression profile, or the first, second and third gene expression profiles; and, optionally, providing the prediction of outcome for the colorectal cancer subject to a healthcare caregiver or to the colorectal cancer subject.

[0235] In some embodiments, the prediction of outcome can also be determined based on the first gene expression profile and the second gene expression profile. In some embodiments, the prediction of outcome can also be determined based on the first gene expression profile and the third gene expression profile. In some embodiments, the prediction of outcome can also be determined based on the second gene expression profile and the third gene expression profile.

[0236] The attached sequence listing, entitled 2020PF00762_Sequence Listing_ST25, is hereby incorporated by reference in its entirety.

Claims

1. 1. A method of predicting outcome in a subject with colorectal cancer, said method comprising: determining or receiving results of the determination of expression levels of six or more genes selected from a first gene expression profile, a second gene expression profile, and a third gene expression profile, wherein the six or more gene expression levels include at least one or more gene expression levels selected from the third gene expression profile, at least one or more gene expression levels selected from the first gene expression profile, and at least one or more gene expression levels selected from the second gene expression profile; the first gene expression profile consists of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; the second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; the third gene expression profile consists of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2; the first, second and third gene expression profiles are determined in a biological sample obtained from the colorectal cancer subject; determining a prognosis of outcome for the colorectal cancer subject based on expression levels of six or more genes; Optionally, providing a prediction of outcome for said colorectal cancer subject to a health care provider or to said colorectal cancer subject. The method comprising:

2. The expression levels of the six or more genes are one or more immune defense response genes, preferably two or more, more preferably three or more, and most preferably all of the immune defense genes; and one or more T cell receptor signaling genes, preferably two or more, more preferably three or more, and most preferably all of the T cell receptor signaling genes; and One or more PDE4D7-related genes, preferably two or more, more preferably three or more, and most preferably all of the PDE4D7-related genes. The method of claim 1 , comprising:

3. determining a prognosis of outcome for the colorectal cancer subject, combining said first gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all, of said immune defense response genes with a regression function derived from a population of colorectal cancer subjects; and / or combining said second gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all, of said T cell receptor signaling genes with a regression function derived from a population of colorectal cancer subjects; and / or combining said third gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7 or all, of said PDE4D7-related genes with a regression function derived from a population of colorectal cancer subjects; and / or combining the expression levels of the six or more genes, e.g., 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more or all of the gene expression levels, with a regression function derived from a population of colorectal cancer subjects. The method according to claim 1 or 2.

4. 4. The method of claim 3, wherein determining the prediction of outcome for the colorectal cancer subject further comprises combining the first combination of gene expression profiles, the second combination of gene expression profiles, and the third combination of gene expression profiles with a regression function derived from a population of colorectal cancer subjects.

5. 5. The method of claim 1, wherein determining an outcome of the colorectal cancer subject is further based on one or more clinical parameters obtained from the colorectal cancer subject.

6. 5. The method of claim 4, wherein the one or more clinical parameters obtained from the colorectal cancer subject include one or more of: (i) a T stage attribute (T1, T2, T3 or T4); (ii) an N stage attribute (N0, N1 or N2); and (iii) an M stage attribute (M0, M1).

7. 7. The method of claim 5 or 6, wherein determining the prediction of outcome for the colorectal cancer subject comprises combining one or more of: (i) the first gene expression profile of the one or more immune defense response genes, (ii) the second gene expression profile of the one or more T cell receptor signaling genes, (iii) the third gene expression profile of the one or more PDE4D7 related genes, and (iv) the combination of the first gene expression profiles, the combination of the second gene expression profiles, the combination of the third gene expression profiles, and the one or more clinical parameters obtained from the colorectal cancer subject, with a regression function derived from a population of colorectal cancer subjects.

8. 8. The method of claim 1 , wherein the biological sample is obtained from the colorectal cancer subject prior to initiation of the treatment.

9. 9. The method of any one of claims 1 to 8, wherein the treatment is surgery, radiation therapy, cytotoxic chemotherapy (CTX), short- or long-term chemoradiotherapy (CRT), immunotherapy or any combination thereof.

10. The prediction of the therapeutic response is unlikely or likely for the efficacy of the treatment, and a treatment is recommended based on the prediction of the therapeutic response, and if the prediction of the therapeutic response is unlikely, the recommended treatment may be: (i) a treatment provided earlier than standard; (ii) radiation therapy with an increased effective dose; (iii) an adjuvant treatment such as chemotherapy; (iv) long-term CRT (chemoradiotherapy); and (iv) an alternative treatment such as immunotherapy. The method of claim 1 , further comprising one or more of the following:

11. 1. An apparatus for predicting outcome in a subject with colorectal cancer, comprising: a first gene expression profile of each of the immune defense response genes, wherein the first gene expression profile is determined in a biological sample obtained from the subject with colorectal cancer, and one or more selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, wherein the first gene expression profile is determined in a biological sample obtained from the subject with colorectal cancer, an input for receiving data indicative of a second gene expression profile of one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, or all T cell receptor signaling genes, determined in a biological sample obtained from the colorectal cancer subject, and a third gene expression profile of one or more, e.g., one, two, three, four, five, six, seven, or all, PDE4D7-associated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, determined in a biological sample obtained from the colorectal cancer subject; A processor for determining a prediction of outcome of the subject with colorectal cancer based on the first gene expression profile, the second gene expression profile and the third gene expression profile in the method of any one of claims 1 to 10; Optionally, a provider for providing a prediction of outcome for said colorectal cancer subject to a healthcare caregiver or to said colorectal cancer subject. An apparatus comprising:

12. 1. A computer program comprising a plurality of instructions, the plurality of instructions being, when run by a computer, A first gene expression profile of each of the immune defense response genes, for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the immune defense response genes, wherein the first gene expression profile is determined in a biological sample obtained from a subject with colorectal cancer, and one or more selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70, for example , 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes, wherein the second gene expression profile is determined in a biological sample obtained from the colorectal cancer subject, and a third gene expression profile of one or more, e.g., 1, 2, 3, 4, 5, 6, 7 or all PDE4D7 associated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2, wherein the third gene expression profile is determined in a biological sample obtained from the colorectal cancer subject; determining a prediction of outcome for the colorectal cancer subject based on the first gene expression profile, or the second gene expression profile, or the third gene expression profile, or the first, second and third gene expression profiles; Optionally, providing a prediction of outcome for said colorectal cancer subject to a health care provider or to said colorectal cancer subject; A computer program comprising:

13. At least one primer and a probe for determining expression levels of six or more genes selected from a first gene expression profile, a second gene expression profile, and a third gene expression profile in a biological sample obtained from a subject, wherein the six or more gene expression levels include at least one or more gene expression levels selected from the third gene expression profile, at least one or more gene expression levels selected from the first gene expression profile, and at least one or more gene expression levels selected from the second gene expression profile; the first gene expression profile consists of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; the second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; At least one primer and probe, wherein the third gene expression profile consists of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2; Optionally, an apparatus according to claim 11 or a computer program according to claim 12; A diagnostic kit comprising:

14. Use of the diagnostic kit according to claim 13.

15. 15. Use of the diagnostic kit according to claim 14 in a method for predicting the outcome of a subject with colorectal cancer.

16. receiving one or more biological samples obtained from a subject with colorectal cancer; 14. A method for determining expression levels of six or more genes selected from a first gene expression profile, a second gene expression profile, and a third gene expression profile, the six or more gene expression levels including at least one or more gene expression levels selected from the third gene expression profile and at least one or more gene expression levels selected from the first and second gene expression profiles, the first gene expression profile consists of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; the second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; the third gene expression profile consists of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2; The method comprising:

17. determining a prediction of outcome for the subject with colorectal cancer based on expression levels of six or more genes; Optionally, providing a prediction of outcome for said colorectal cancer subject to a health care provider or to said colorectal cancer subject. Use of expression levels of six or more genes selected from a first gene expression profile, a second gene expression profile, and a third gene expression profile in the method of predicting the outcome of a subject with colorectal cancer, comprising: the six or more gene expression levels include at least one or more gene expression levels selected from the third gene expression profile and at least one or more gene expression levels selected from the first and second gene expression profiles, and the first gene expression profile consists of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1; 20. The method of claim 19, wherein the second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, and the third gene expression profile consists of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

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