PATENT ABSTRACT
The present invention relates to methods, kits and systems for the prognosis of the disease outcome of breast cancer, said method comprising:
       (a) determining in a tumor sample from said patient the RNA expression levels of at least 2 of the following 9 genes: UBE2C, BIRC5, RACGAP1, DHCR7, STC2, AZGP1, RBBP8, IL6ST, and MGP   (b) mathematically combining expression level values for the genes of the said set which values were determined in the tumor sample to yield a combined score, wherein said combined score is indicative of a prognosis of said patient; and kits and systems for performing said method.

PATENT DESCRIPTION
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a continuation of U.S. Ser. No. 13/638,360, filed Nov. 21, 2012, which is a U.S. National Stage Patent of PCT Application No. PCT/EP2011/054855, filed Mar. 29, 2011, which claims the benefit of European Patent Application No. 10158561.0, filed Mar. 31, 2010, all of which are hereby incorporated by reference in their entireties. 
     
    
     TECHNICAL FIELD 
       [0002]    The present invention relates to methods, kits and systems for the prognosis of the disease outcome of breast cancer. More specific, the present invention relates to the prognosis of breast cancer based on measurements of the expression levels of marker genes in tumor samples of breast cancer patients. 
       BACKGROUND OF THE INVENTION 
       [0003]    Breast cancer is one of the leading causes of cancer death in women in western countries. More specifically breast cancer claims the lives of approximately 40,000 women and is diagnosed in approximately 200,000 women annually in the United States alone. Over the last few decades, adjuvant systemic therapy has led to markedly improved survival in early breast cancer. This clinical experience has led to consensus recommendations offering adjuvant systemic therapy for the vast majority of breast cancer patients (EBCAG). In breast cancer a multitude of treatment options are available which can be applied in addition to the routinely performed surgical removal of the tumor and subsequent radiation of the tumor bed. Three main and conceptually different strategies are endocrine treatment, chemotherapy and treatment with targeted therapies. Prerequisite for treatment with endocrine agents is expression of hormone receptors in the tumor tissue i.e. either estrogen receptor, progesterone receptor or both. Several endocrine agents with different mode of action and differences in disease outcome when tested in large patient cohorts are available. Tamoxifen has been the mainstay of endocrine treatment for the last three decades. Large clinical trials showed that tamoxifen significantly reduced the risk of tumor recurrence. An additional treatment option is based on aromatase inhibitors which belong to a new endocrine drug class. In contrast to tamoxifen which is a competitive inhibitor of estrogen binding aromatase inhibitors block the production of estrogen itself thereby reducing the growth stimulus for estrogen receptor positive tumor cells. Still, some patients experience a relapse despite endocrine treatment and in particular these patients might benefit from additional therapeutic drugs. Chemotherapy with anthracyclines, taxanes and other agents have been shown to be efficient in reducing disease recurrence in estrogen receptor positive as well as estrogen receptor negative patients. The NSABP-20 study compared tamoxifen alone against tamoxifen plus chemotherapy in node negative estrogen receptor positive patients and showed that the combined treatment was more effective than tamoxifen alone. However, the IBCSG IX study comparing tamoxifen alone against tamoxifen plus chemotherapy failed to show any significant benefit for the addition of cytotoxic agents. Recently, a systemically administered antibody directed against the HER2/neu antigen on the surface of tumor cells have been shown to reduce the risk of recurrence several fold in a patients with Her2neu over expressing tumors. Yet, most if not all of the different drug treatments have numerous potential adverse effects which can severely impair patients&#39; quality of life (Shapiro and Recht, 2001; Ganz et al., 2002). This makes it mandatory to select the treatment strategy on the basis of a careful risk assessment for the individual patient to avoid over-as well as under treatment. Since the benefit of chemotherapy is relatively large in HER2/neu positive and tumors characterized by absence of HER2/neu and estrogen receptor expression (basal type), compared to HER2/neu negative and estrogen receptor positive tumors (luminal type), the most challenging treatment decision concerns luminal tumors for which classical clinical factors like grading, tumor size or lymph node involvement do not provide a clear answer to the question whether to use chemotherapy or not. Newer molecular tools like a 21 gene assay, a genomic grade index assay and others have been developed to address this medical need. 
         [0004]    Treatment guidelines are usually developed by renowned experts in the field. In Europe the St Gallen guidelines from the year 2009 recommend chemotherapy to patients with HER2 positive breast cancer as well as to patients with HER2 negative and ER negative disease. Uncertainty about the usefulness of chemotherapy arises in patients with HER2 negative and ER positive disease. In order to make a balanced treatment decision for the individual the likelihood of cancer recurrence is used as the most useful criteria. Clinical criteria like lypmph node status, tumor grading, tumor size and others are helpful since they provide information about the risk of recurrence. More recently, multigene assays have been shown to provide information superior or additional to the standard clinical risk factors. It is generally recognized, that proliferation markers seem to provide the dominant prognostic information. Prominent examples of those predictors are the Mammaprint test from Agendia, the Relapse Score from Veridex and the Genomic Grade Index, developed at the institute Jules Bordet and licensed to Ipsogen. All of these assays are based on determination of the expression levels of at least 70 genes and all have been developed for RNA not heavily degraded by formalin fixation and paraffin embedding, but isolated from fresh tissue (shipped in RNALater™). Another prominent multigene assay is the Recurrence Score test of Genomic Health Inc. The test determines the expression level of 16 cancer related genes and 5 reference genes after RNA extraction from formalin fixed and paraffin embedded tissue samples. 
         [0005]    However, the current tools suffer from a lack of clinical validity and utility in the most important clinical risk group, i.e. those breast cancer patients of intermediate risk of recurrence based on standard clinical parameter. Therefore, better tools are needed to optimize treatment decisions based on patient prognosis. For the clinical utility of avoiding chemotherapy, a test with a high sensitivity and high negative predictive value is needed, in order not to undertreat a patient that eventually develops a distant metastasis after surgery. In regard to the continuing need for materials and methods useful in making clinical decisions on adjuvant therapy, the present invention fulfills the need for advanced methods for the prognosis of breast cancer on the basis of readily accessible clinical and experimental data. 
       DEFINITIONS 
       [0006]    Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. 
         [0007]    The term “cancer” is not limited to any stage, grade, histomorphological feature, aggressivity, or malignancy of an affected tissue or cell aggregation. 
         [0008]    The term “predicting an outcome” of a disease, as used herein, is meant to include both a prediction of an outcome of a patient undergoing a given therapy and a prognosis of a patient who is not treated. The term “predicting an outcome” may, in particular, relate to the risk of a patient developing metastasis, local recurrence or death. 
         [0009]    The term “prediction”, as used herein, relates to an individual assessment of the malignancy of a tumor, or to the expected survival rate (OAS, overall survival or DFS, disease free survival) of a patient, if the tumor is treated with a given therapy. In contrast thereto, the term “prognosis” relates to an individual assessment of the malignancy of a tumor, or to the expected survival rate (OAS, overall survival or DFS, disease free survival) of a patient, if the tumor remains untreated. 
         [0010]    An “outcome” within the meaning of the present invention is a defined condition attained in the course of the disease. This disease outcome may e.g. be a clinical condition such as “recurrence of disease”, “development of metastasis”, “development of nodal metastasis”, “development of distant metastasis”, “survival”, “death”, “tumor remission rate”, a disease stage or grade or the like. 
         [0011]    A “risk” is understood to be a number related to the probability of a subject or a patient to develop or arrive at a certain disease outcome. The term “risk” in the context of the present invention is not meant to carry any positive or negative connotation with regard to a patient&#39;s wellbeing but merely refers to a probability or likelihood of an occurrence or development of a given condition. 
         [0012]    The term “clinical data” relates to the entirety of available data and information concerning the health status of a patient including, but not limited to, age, sex, weight, menopausal/hormonal status, etiopathology data, anamnesis data, data obtained by in vitro diagnostic methods such as histopathology, blood or urine tests, data obtained by imaging methods, such as x-ray, computed tomography, MRI, PET, spect, ultrasound, electrophysiological data, genetic analysis, gene expression analysis, biopsy evaluation, intraoperative findings. 
         [0013]    The term “node positive”, “diagnosed as node positive”, “node involvement” or “lymph node involvement” means a patient having previously been diagnosed with lymph node metastasis. It shall encompass both draining lymph node, near lymph node, and distant lymph node metastasis. This previous diagnosis itself shall not form part of the inventive method. Rather it is a precondition for selecting patients whose samples may be used for one embodiment of the present invention. This previous diagnosis may have been arrived at by any suitable method known in the art, including, but not limited to lymph node removal and pathological analysis, biopsy analysis, in-vitro analysis of biomarkers indicative for metastasis, imaging methods (e.g. computed tomography, X-ray, magnetic resonance imaging, ultrasound), and intraoperative findings. 
         [0014]    In the context of the present invention a “biological sample” is a sample which is derived from or has been in contact with a biological organism. Examples for biological samples are: cells, tissue, body fluids, lavage fluid, smear samples, biopsy specimens, blood, urine, saliva, sputum, plasma, serum, cell culture supernatant, and others. 
         [0015]    A “tumor sample” is a biological sample containing tumor cells, whether intact or degraded. The sample may be of any biological tissue or fluid. Such samples include, but are not limited to, sputum, blood, serum, plasma, blood cells (e.g., white cells), tissue, core or fine needle biopsy samples, cell-containing body fluids, urine, peritoneal fluid, and pleural fluid, liquor cerebrospinalis, tear fluid, or cells isolated therefrom. This may also include sections of tissues such as frozen or fixed sections taken for histological purposes or microdissected cells or extracellular parts thereof. A tumor sample to be analyzed can be tissue material from a neoplastic lesion taken by aspiration or punctuation, excision or by any other surgical method leading to biopsy or resected cellular material. Such comprises tumor cells or tumor cell fragments obtained from the patient. The cells may be found in a cell “smear” collected, for example, by a nipple aspiration, ductal lavage, fine needle biopsy or from provoked or spontaneous nipple discharge. In another embodiment, the sample is a body fluid. Such fluids include, for example, blood fluids, serum, plasma, lymph, ascitic fluids, gynecologic fluids, or urine but not limited to these fluids. 
         [0016]    A “gene” is a set of segments of nucleic acid that contains the information necessary to produce a functional RNA product. A “gene product” is a biological molecule produced through transcription or expression of a gene, e.g. an mRNA, cDNA or the translated protein. 
         [0017]    An “mRNA” is the transcribed product of a gene and shall have the ordinary meaning understood by a person skilled in the art. A “molecule derived from an mRNA” is a molecule which is chemically or enzymatically obtained from an mRNA template, such as cDNA. 
         [0018]    The term “expression level” refers to a determined level of gene expression. This may be a determined level of gene expression as an absolute value or compared to a reference gene (e.g. a housekeeping gene), to the average of two or more reference genes, or to a computed average expression value (e.g. in DNA chip analysis) or to another informative gene without the use of a reference sample. The expression level of a gene may be measured directly, e.g. by obtaining a signal wherein the signal strength is correlated to the amount of mRNA transcripts of that gene or it may be obtained indirectly at a protein level, e.g. by immunohistochemistry, CISH, ELISA or RIA methods. The expression level may also be obtained by way of a competitive reaction to a reference sample. An expression value which is determined by measuring some physical parameter in an assay, e.g. fluorescence emission, may be assigned a numerical value which may be used for further processing of information. 
         [0019]    A “reference pattern of expression levels”, within the meaning of the invention shall be understood as being any pattern of expression levels that can be used for the comparison to another pattern of expression levels. In a preferred embodiment of the invention, a reference pattern of expression levels is, e.g., an average pattern of expression levels observed in a group of healthy individuals, diseased individuals, or diseased individuals having received a particular type of therapy, serving as a reference group, or individuals with good or bad outcome. 
         [0020]    The term “mathematically combining expression levels”, within the meaning of the invention shall be understood as deriving a numeric value from a determined expression level of a gene and applying an algorithm to one or more of such numeric values to obtain a combined numerical value or combined score. 
         [0021]    An “algorithm” is a process that performs some sequence of operations to produce information. 
         [0022]    A “score” is a numeric value that was derived by mathematically combining expression levels using an algorithm. It may also be derived from expression levels and other information, e.g. clinical data. A score may be related to the outcome of a patient&#39;s disease. 
         [0023]    A “discriminant function” is a function of a set of variables used to classify an object or event. A discriminant function thus allows classification of a patient, sample or event into a category or a plurality of categories according to data or parameters available from said patient, sample or event. Such classification is a standard instrument of statistical analysis well known to the skilled person. E.g. a patient may be classified as “high risk” or “low risk”, “high probability of metastasis” or “low probability of metastasis”, “in need of treatment” or “not in need of treatment” according to data obtained from said patient, sample or event. Classification is not limited to “high vs. low”, but may be performed into a plurality of categories, grading or the like. Classification shall also be understood in a wider sense as a discriminating score, where e.g. a higher score represents a higher likelihood of distant metastasis, e.g. the (overall) risk of a distant metastasis. Examples for discriminant functions which allow a classification include, but are not limited to functions defined by support vector machines (SVM), k-nearest neighbors (kNN), (naive) Bayes models, linear regression models or piecewise defined functions such as, for example, in subgroup discovery, in decision trees, in logical analysis of data (LAD) and the like. In a wider sense, continuous score values of mathematical methods or algorithms, such as correlation coefficients, projections, support vector machine scores, other similarity-based methods, combinations of these and the like are examples for illustrative purpose. 
         [0024]    The term “therapy modality”, “therapy mode”, “regimen” as well as “therapy regimen” refers to a timely sequential or simultaneous administration of anti-tumor, and/or anti vascular, and/or immune stimulating, and/or blood cell proliferative agents, and/or radiation therapy, and/or hyperthermia, and/or hypothermia for cancer therapy. The administration of these can be performed in an adjuvant and/or neoadjuvant mode. The composition of such “protocol” may vary in the dose of the single agent, timeframe of application and frequency of administration within a defined therapy window. Currently various combinations of various drugs and/or physical methods, and various schedules are under investigation. 
         [0025]    The term “cytotoxic chemotherapy” refers to various treatment modalities affecting cell proliferation and/or survival. The treatment may include administration of alkylating agents, antimetabolites, anthracyclines, plant alkaloids, topoisomerase inhibitors, and other antitumor agents, including monoclonal antibodies and kinase inhibitors. In particular, the cytotoxic treatment may relate to a taxane treatment. Taxanes are plant alkaloids which block cell division by preventing microtubule function. The prototype taxane is the natural product paclitaxel, originally known as Taxol and first derived from the bark of the Pacific Yew tree. Docetaxel is a semi-synthetic analogue of paclitaxel. Taxanes enhance stability of microtubules, preventing the separation of chromosomes during anaphase. 
         [0026]    The term “endocrine treatment” or “hormonal treatment” (sometimes also referred to as “anti-hormonal treatment”) denotes a treatment which targets hormone signalling, e.g. hormone inhibition, hormone receptor inhibition, use of hormone receptor agonists or antagonists, use of scavenger- or orphan receptors, use of hormone derivatives and interference with hormone production. Particular examples are tamoxifene therapy which modulates signalling of the estrogen receptor, or aromatase treatment which interferes with steroid hormone production. 
         [0027]    Tamoxifen is an orally active selective estrogen receptor modulator (SERM) that is used in the treatment of breast cancer and is currently the world&#39;s largest selling drug for that purpose. Tamoxifen is sold under the trade names Nolvadex, Istubal, and Valodex. However, the drug, even before its patent expiration, was and still is widely referred to by its generic name “tamoxifen.” Tamoxifen and Tamoxifen derivatives competitively bind to estrogen receptors on tumors and other tissue targets, producing a nuclear complex that decreases RNA synthesis and inhibits estrogen effects. 
         [0028]    Steroid receptors are intracellular receptors (typically cytoplasmic) that perform signal transduction for steroid hormones. Examples include type I Receptors, in particular sex hormone receptors, e.g. androgen receptor, estrogen receptor, progesterone receptor; Glucocorticoid receptor, mineralocorticoid receptor; and type II Receptors, e.g. vitamin A receptor, vitamin D receptor, retinoid receptor, thyroid hormone receptor. 
         [0029]    The term “hybridization-based method”, as used herein, refers to methods imparting a process of combining complementary, single-stranded nucleic acids or nucleotide analogues into a single double stranded molecule. Nucleotides or nucleotide analogues will bind to their complement under normal conditions, so two perfectly complementary strands will bind to each other readily. In bioanalytics, very often labeled, single stranded probes are used in order to find complementary target sequences. If such sequences exist in the sample, the probes will hybridize to said sequences which can then be detected due to the label. Other hybridization based methods comprise microarray and/or biochip methods. Therein, probes are immobilized on a solid phase, which is then exposed to a sample. If complementary nucleic acids exist in the sample, these will hybridize to the probes and can thus be detected. These approaches are also known as “array based methods”. Yet another hybridization based method is PCR, which is described below. When it comes to the determination of expression levels, hybridization based methods may for example be used to determine the amount of mRNA for a given gene. 
         [0030]    An oligonucleotide capable of specifically binding sequences a gene or fragments thereof relates to an oligonucleotide which specifically hybridizes to a gene or gene product, such as the gene&#39;s mRNA or cDNA or to a fragment thereof. To specifically detect the gene or gene product, it is not necessary to detect the entire gene sequence. A fragment of about 20-150 bases will contain enough sequence specific information to allow specific hybridization. 
         [0031]    The term “a PCR based method” as used herein refers to methods comprising a polymerase chain reaction (PCR). This is a method of exponentially amplifying nucleic acids, e.g. DNA by enzymatic replication in vitro. As PCR is an in vitro technique, it can be performed without restrictions on the form of DNA, and it can be extensively modified to perform a wide array of genetic manipulations. When it comes to the determination of expression levels, a PCR based method may for example be used to detect the presence of a given mRNA by (1) reverse transcription of the complete mRNA pool (the so called transcriptome) into cDNA with help of a reverse transcriptase enzyme, and (2) detecting the presence of a given cDNA with help of respective primers. This approach is commonly known as reverse transcriptase PCR (rtPCR). 
         [0032]    Moreover, PCR-based methods comprise e.g. real time PCR, and, particularly suited for the analysis of expression levels, kinetic or quantitative PCR (qPCR). 
         [0033]    The term “Quantitative PCR” (qPCR)” refers to any type of a PCR method which allows the quantification of the template in a sample. Quantitative real-time PCR comprise different techniques of performance or product detection as for example the TaqMan technique or the LightCycler technique. The TaqMan technique, for examples, uses a dual-labelled fluorogenic probe. The TaqMan real-time PCR measures accumulation of a product via the fluorophore during the exponential stages of the PCR, rather than at the end point as in conventional PCR. The exponential increase of the product is used to determine the threshold cycle, CT, i.e. the number of PCR cycles at which a significant exponential increase in fluorescence is detected, and which is directly correlated with the number of copies of DNA template present in the reaction. The set up of the reaction is very similar to a conventional PCR, but is carried out in a real-time thermal cycler that allows measurement of fluorescent molecules in the PCR tubes. Different from regular PCR, in TaqMan real-time PCR a probe is added to the reaction, i.e., a single-stranded oligonucleotide complementary to a segment of 20-60 nucleotides within the DNA template and located between the two primers. A fluorescent reporter or fluorophore (e.g., 6-carboxyfluorescein, acronym: FAM, or tetrachlorofluorescin, acronym: TET) and quencher (e.g., tetramethylrhodamine, acronym: TAMRA, of dihydrocyclopyrroloindole tripeptide ‘black hole quencher’, acronym: BHQ) are covalently attached to the 5′ and 3′ ends of the probe, respectively[2]. The close proximity between fluorophore and quencher attached to the probe inhibits fluorescence from the fluorophore. During PCR, as DNA synthesis commences, the 5′ to 3′ exonuclease activity of the Taq polymerase degrades that proportion of the probe that has annealed to the template. Degradation of the probe releases the fluorophore from it and breaks the close proximity to the quencher, thus relieving the quenching effect and allowing fluorescence of the fluorophore. Hence, fluorescence detected in the real-time PCR thermal cycler is directly proportional to the fluorophore released and the amount of DNA template present in the PCR. 
         [0034]    By “array” or “matrix” an arrangement of addressable locations or “addresses” on a device is meant. The locations can be arranged in two dimensional arrays, three dimensional arrays, or other matrix formats. The number of locations can range from several to at least hundreds of thousands. Most importantly, each location represents a totally independent reaction site. Arrays include but are not limited to nucleic acid arrays, protein arrays and antibody arrays. A “nucleic acid array” refers to an array containing nucleic acid probes, such as oligonucleotides, nucleotide analogues, polynucleotides, polymers of nucleotide analogues, morpholinos or larger portions of genes. The nucleic acid and/or analogue on the array is preferably single stranded. Arrays wherein the probes are oligonucleotides are referred to as “oligo         nucleotide arrays” or “oligonucleotide chips.” A “microarray,” herein also refers to a “biochip” or “biological chip”, an array of regions having a density of discrete regions of at least about 100/cm2, and preferably at least about 1000/cm2. 
         [0035]    “Primer pairs” and “probes”, within the meaning of the invention, shall have the ordinary meaning of this term which is well known to the person skilled in the art of molecular biology. In a preferred embodiment of the invention “primer pairs” and “probes”, shall be understood as being polynucleotide molecules having a sequence identical, complementary, homologous, or homologous to the complement of regions of a target polynucleotide which is to be detected or quantified. In yet another embodiment, nucleotide analogues are also comprised for usage as primers and/or probes. Probe technologies used for kinetic or real time PCR applications could be e.g. TaqMan® systems obtainable at Applied Biosystems, extension probes such as Scorpion® Primers, Dual Hybridisation Probes, Amplifluor® obtainable at Chemicon International, Inc, or Minor Groove Binders. 
         [0036]    “Individually labeled probes”, within the meaning of the invention, shall be understood as being molecular probes comprising a polynucleotide, oligonucleotide or nucleotide analogue and a label, helpful in the detection or quantification of the probe. Preferred labels are fluorescent molecules, luminescent molecules, radioactive molecules, enzymatic molecules and/or quenching molecules. 
         [0037]    “Arrayed probes”, within the meaning of the invention, shall be understood as being a collection of immobilized probes, preferably in an orderly arrangement. In a preferred embodiment of the invention, the individual “arrayed probes” can be identified by their respective position on the solid support, e.g., on a “chip”. 
         [0038]    When used in reference to a single-stranded nucleic acid sequence, the term “substantially homologous” refers to any probe that can hybridize (i.e., it is the complement of) the single-stranded nucleic acid sequence under conditions of low stringency as described above. 
       SUMMARY OF THE INVENTION 
       [0039]    In general terms, the present invention provides a method to assess the risk of recurrence of a node negative or positive, estrogen receptor positive and HER2/NEU negative breast cancer patient, in particular patients receiving endocrine therapy, for example when treated with tamoxifen. Estrogen receptor status is generally determined using immunohistochemistry, HER2/NEU (ERBB2) status is generally determined using immunohistochemistry and fluorescence in situ hybridization. However, estrogen receptor status and HER2/NEU (ERBB2) status may, for the purposes of the invention, be determined by any suitable method, e.g. immunohistochemistry, fluorescence in situ hybridization (FISH), or RNA expression analysis. 
         [0040]    The present invention relates to a method for predicting an outcome of breast cancer in an estrogen receptor positive and HER2 negative tumor of a breast cancer patient, said method comprising:
       (a) determining in a tumor sample from said patient the RNA expression levels of at least 2 of the following 9 genes: UBE2C, BIRC5, RACGAP1, DHCR7, STC2, AZGP1, RBBP8, IL6ST, and MGP   (b) mathematically combining expression level values for the genes of the said set which values were determined in the tumor sample to yield a combined score, wherein said combined score is indicative of a prognosis of said patient. In one embodiment at least 3, 4, 5 or 6 genes are selected.       
 
         [0043]    In a further embodiment of the invention the method comprises:
       (a) determining in a tumor sample from said patient the RNA expression levels of the following 8 genes: UBE2C, RACGAP1, DHCR7, STC2, AZGP1, RBBP8, IL6ST, and MGP   (b) mathematically combining expression level values for the genes of the said set which values were determined in the tumor sample to yield a combined score, wherein said combined score is indicative of a prognosis of said patient.       
 
         [0046]    In a further embodiment the method of the invention comprises:
       (a) determining in a tumor sample from said patient the RNA expression levels of the following 8 genes: UBE2C, BIRC5, DHCR7, STC2, AZGP1, RBBP8, IL6ST, and MGP;   (b) mathematically combining expression level values for the genes of the said set which values were determined in the tumor sample to yield a combined score, wherein said combined score is indicative of a prognosis of said patient.       
 
         [0049]    In yet another embodiment of the invention 
         [0050]    BIRC5 may be replaced by UBE2C or TOP2A or RACGAP1 or AURKA or NEK2 or E2F8 or PCNA or CYBRD1 or DCN or ADRA2A or SQLE or CXCL12 or EPHX2 or ASPH or PRSS16 or EGFR or CCND1 or TRIM29 or DHCR7 or PIP or TFAP2B or WNT5A or APOD or PTPRT with the proviso that after a replacement 8 different genes are selected; and 
         [0051]    UBE2C may be replaced by BIRC5 or RACGAP1 or TOP2A or AURKA or NEK2 or E2F8 or PCNA or CYBRD1 or ADRA2A or DCN or SQLE or CCND1 or ASPH or CXCL12 or PIP or PRSS16 or EGFR or DHCR7 or EPHX2 or TRIM29 with the proviso that after a replacement 8 different genes are selected; and 
         [0052]    DHCR7 may be replaced by AURKA, BIRC5, UBE2C or by any other gene that may replace BIRC5 or UBE2C with the proviso that after a replacement 8 different genes are selected; and 
         [0053]    STC2 may be replaced by INPP4B or IL6ST or SEC14L2 or MAPT or CHPT1 or ABAT or SCUBE2 or ESR1 or RBBP8 or PGR or PTPRT or HSPA2 or PTGER3 with the proviso that after a replacement 8 different genes are selected; and 
         [0054]    AZGP1 may be replaced by PIP or EPHX2 or PLAT or SEC14L2 or SCUBE2 or PGR with the proviso that after a replacement 8 different genes are selected; and 
         [0055]    RBBP8 may be replaced by CELSR2 or PGR or STC2 or ABAT or IL6ST with the proviso that after a replacement 8 different genes are selected; and 
         [0056]    IL6ST may be replaced by INPP4B or STC2 or MAPT or SCUBE2 or ABAT or PGR or SEC14L2 or ESR1 or GJA1 or MGP or EPHX2 or RBBP8 or PTPRT or PLAT with the proviso that after a replacement 8 different genes are selected; and 
         [0057]    MGP may be replaced by APOD or IL6ST or EGFR with the proviso that after a replacement 8 different genes are selected. 
         [0058]    According to an aspect of the invention there is provided a method as described above, wherein said combined score is indicative of benefit from cytotoxic chemotherapy. 
         [0059]    Using the method of the invention before a patient receives endocrine therapy allows a prediction of the efficacy of endocrine therapy. 
         [0060]    Table 2 below shows whether the overexpression of each of the above marker genes is indicative of a good outcome or a bad outcome in a patient receiving endocrine therapy. The skilled person can thus construct a mathematical combination i.e. an algorithm taking into account the effect of a given genes. For example a summation or weighted summation of genes whose overexpression is indicative of a good outcome results in an algorithm wherein a high risk score is indicative of a good outcome. The validity of the algorithm may be examined by analyzing tumor samples of patients with a clinical record, wherein e.g. the score for good outcome patients and bad outcome patients may be determined separately and compared. The skilled person, a biostatistician, will know to apply further mathematical methods, such as discriminate functions to obtain optimized algorithms. Algorithms may be optimized e.g. for sensitivity or specificity. Algorithms may be adapted to the particular analytical platform used to measure gene expression of marker genes, such as quantitiative PCR. 
         [0061]    According to an aspect of the invention there is provided a method as described above, wherein said endocrine therapy comprises tamoxifen or an aromatase inhibitor. 
         [0062]    According to an aspect of the invention there is provided a method as described above, wherein a risk of developing recurrence is predicted. 
         [0063]    According to an aspect of the invention there is provided a method as described above, wherein said expression level is determined as a non-protein expression level. 
         [0064]    According to an aspect of the invention there is provided a method as described above, wherein said expression level is determined as an RNA expression level. 
         [0065]    According to an aspect of the invention there is provided a method as described above, wherein said expression level is determined by at least one of
       a PCR based method,   a micorarray based method, and   a hybridization based method.       
 
         [0069]    According to an aspect of the invention there is provided a method as described above, wherein said determination of expression levels is in a formalin-fixed paraffin embedded tumor sample or in a fresh-frozen tumor sample. 
         [0070]    According to an aspect of the invention there is provided a method as described above, wherein the expression level of said at least on marker gene is determined as a pattern of expression relative to at least one reference gene or to a computed average expression value. 
         [0071]    According to an aspect of the invention there is provided a method as described above, wherein said step of mathematically combining comprises a step of applying an algorithm to values representative of an expression level of a given gene. 
         [0072]    According to an aspect of the invention there is provided a method as described above, wherein said algorithm is a linear combination of said values representative of an expression level of a given gene. 
         [0073]    According to an aspect of the invention there is provided a method as described above, wherein a value for a representative of an expression level of a given gene is multiplied with a coefficient. 
         [0074]    According to an aspect of the invention there is provided a method as described above, wherein one, two or more thresholds are determined for said combined score and discriminated into high and low risk, high, intermediate and low risk, or more risk groups by applying the threshold on the combined score. 
         [0075]    According to an aspect of the invention there is provided a method as described above, wherein a high combined score is indicative of benefit from a more aggressive therapy, e.g. cytotoxic chemotherapy. The skilled person understands that a “high score” in this regard relates to a reference value or cutoff value. The skilled person further understands that depending on the particular algorithm used to obtain the combined score, also a “low” score below a cut off or reference value can be indicative of benefit from a more aggressive therapy, e.g. cytotoxic chemotherapy. This is the case when genes having a positive correlation with high risk of metastasis factor into the algorithm with a positive coefficient, such that an overall high score indicates high expression of genes having a positive correlation with high risk. 
         [0076]    According to an aspect of the invention there is provided a method as described above, wherein an information regarding nodal status of the patient is processed in the step of mathematically combining expression level values for the genes to yield a combined score. 
         [0077]    According to an aspect of the invention there is provided a method as described above, wherein said information regarding nodal status is a numerical value ≦0 if said nodal status is negative and said information is a numerical value &gt;0 if said nodal status positive or unknown. In exemplary embodiments of the invention a negative nodal status is assigned the value 0, an unknown nodal status is assigned the value 0.5 and a positive nodal status is assigned the value 1. Other values may be chosen to reflect a different weighting of the nodal status within an algorithm. 
         [0078]    The invention further relates to a kit for performing a method as described above, said kit comprising a set of oligonucleotides capable of specifically binding sequences or to sequences of fragments of the genes in a combination of genes, wherein
       (i) said combination comprises at least the 8 genes UBE2C, BIRC5, DHCR7, STC2, AZGP1, RBBP8, IL6ST, and MGP; or   (ii) said combination comprises at least the 10 genes BIRC5, AURKA, PVALB, NMU, STC2, RBBP8, PTGER3, CXCL12, CDH1, and PIP; or   (iii) said combination comprises at least the 9 genes BIRC5, DHCR7, RACGAP1, PVALB, STC2, IL6ST, PTGER3, CXCL12, and ABAT; or   (iv) said combination comprises at least the 9 genes DHCR7, RACGAP1, NMU, AZGP1, RBBP8, IL6ST, and MGP;       
 
         [0083]    The invention further relates to the use of a kit for performing a method of any of claims  1  to  17 , said kit comprising a set of oligonucleotides capable of specifically binding sequences or to sequences of fragments of the genes in a combination of genes, wherein
       (i) said combination comprises at least the 8 genes UBE2C, BIRC5, DHCR7, STC2, AZGP1, RBBP8, IL6ST, and MGP; or   (ii) said combination comprises at least the 10 genes BIRC5, AURKA, PVALB, NMU, STC2, RBBP8, PTGER3, CXCL12, CDH1, and PIP; or   (iii) said combination comprises at least the 9 genes BIRC5, DHCR7, RACGAP1, PVALB, STC2, IL6ST, PTGER3, CXCL12, and ABAT; or   (iv) said combination comprises at least the 9 genes DHCR7, RACGAP1, NMU, AZGP1, RBBP8, IL6ST, and MGP;19. A computer program product capable of processing values representative of an expression level of the genes AKR1C3, MAP4 and SPP1 by mathematically combining said values to yield a combined score, wherein said combined score is indicative of benefit from cytotoxic chemotherapy of said patient.       
 
         [0088]    The invention further relates to a computer program product capable of processing values representative of an expression level of a combination of genes mathematically combining said values to yield a combined score, wherein said combined score is indicative of efficacy or benefit from endocrine therapy of said patient, according to the above methods. 
         [0089]    Said computer program product may be stored on a data carrier or implemented on a diagnostic system capable of outputting values representative of an expression level of a given gene, such as a real time PCR system. 
         [0090]    If the computer program product is stored on a data carrier or running on a computer, operating personal can input the expression values obtained for the expression level of the respective genes. The computer program product can then apply an algorithm to produce a combined score indicative of benefit from cytotoxic chemotherapy for a given patient. 
         [0091]    The methods of the present invention have the advantage of providing a reliable prediction of an outcome of disease based on the use of only a small number of genes. The methods of the present invention have been found to be especially suited for analyzing the response to endocrine treatment, e.g. by tamoxifen, of patients with tumors classified as ESR1 positive and ERBB2 negative. 
     
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         [0092]      FIG. 1  shows a Forrest Plot of the adjusted hazard unit ratio with 95% confidence interval of the T5 score in the combined cohort, as well as the individual treatment arms of the ABCSG06 and 08 studies, using distant metastasis as endpoint. 
           [0093]      FIG. 2  shows a Kaplan Meier Analysis of ER+, HER−, N0-3 patients from the combined ABCSG06 and 08 cohorts, stratified as high or low risk according to T5 Score value. 
           [0094]      FIG. 3  shows joint distribution scatter plot of expressions in training data. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0095]    The invention is explained in conjunction with exemplary embodiments and the attached figures: 
         [0096]    Herein disclosed are unique combinations of marker genes which can be combined into an algorithm for the here presented new predictive test. 
         [0097]    Technically, the method of the invention can be practiced using two technologies: 1.) Isolation of total RNA from fresh or fixed tumor tissue and 2.) Kinetic RT-PCR of the isolated nucleic acids. Alternatively, it is contemplated to measure expression levels using alternative technologies, e.g by microarray or by measurement at a protein level. 
         [0098]    The methods of the invention are based on quantitative determination of RNA species isolated from the tumor in order to obtain expression values and subsequent bioinformatic analysis of said determined expression values. RNA species might be isolated from any type of tumor sample, e.g. biopsy samples, smear samples, resected tumor material, fresh frozen tumor tissue or from paraffin embedded and formalin fixed tumor tissue. First, RNA levels of genes coding for specific combinations of the genes UBE2C, BIRC5, DHCR7, RACGAP1, AURKA, PVALB, NMU, STC2, AZGP1, RBBP8, IL6ST, MGP, PTGER3, CXCL12, ABAT, CDH1, and PIP or specific combinations thereof, as indicated, are determined. Based on these expression values a prognostic score is calculated by a mathematical combination, e.g. according to formulas T5 T1, T4, or T5b (see below). A high score value indicates a high risk for development of distant metastasis, a low score value indicates a low risk of distant metastasis. Consequently, a high score also indicates that the patient is a high risk patient who will benefit from a more aggressive therapy, e.g. cytotoxic chemotherapy. 
         [0099]    The present examples are based on identification of prognostic genes using tumors of patients homogeneously treated in the adjuvant setting with tamoxifen. Furthermore, identification of relevant genes has been restricted to tumors classified as ESR1 positive and ERBB2 negative based on RNA expression levels. In addition, genes allowing separation of intermediate risk, e.g. grade 2 tumors were considered for algorithm development. Finally, a platform transfer from Affymetrix HG_U133a arrays to quantitative real time PCR, as well as a sample type transfer from fresh frozen tissue to FFPE tissue was performed to ensure robust algorithm performance, independent from platform and tissue type. As a result, determination of the expression level of RNA species from the primary tumor and the subsequent complex and multivariate analysis as described above provides a superior method for prediction of the likelihood of disease recurrence in patients diagnosed with lymph node negative or positive early breast cancer, when treated with tamoxifen only in the adjuvant setting. Thus the test relies on fewer genes than those of the competitors but provides superior information regarding high sensitivity and negative predictive value, in particular for tumors considered to exhibit an intermediate risk of recurrence based on standard clinical factors. 
         [0100]    The total RNA was extracted with a Siemens, silica bead-based and fully automated isolation method for RNA from one 10 μm whole FFPE tissue section on a Hamilton MICROLAB STARlet liquid handling robot (17). The robot, buffers and chemicals were part of a Siemens VERSANT® kPCR Molecular System (Siemens Healthcare Diagnostics, Tarrytown, N.Y.; not commercially available in the USA). Briefly, 150 μl FFPE buffer (Buffer FFPE, research reagent, Siemens Healthcare Diagnostics) were added to each section and incubated for 30 minutes at 80° C. with shaking to melt the paraffin. After cooling down, proteinase K was added and incubated for 30 minutes at 65° C. After lysis, residual tissue debris was removed from the lysis fluid by a 15 minutes incubation step at 65° C. with 40 μl silica-coated iron oxide beads. The beads with surface-bound tissue debris were separated with a magnet and the lysates were transferred to a standard 2 ml deep well-plate (96 wells). There, the total RNA and DNA was bound to 40 μl unused beads and incubated at room temperature. Chaotropic conditions were produced by the addition of 600 μl lysis buffer. Then, the beads were magnetically separated and the supernatants were discarded. Afterwards, the surface-bound nucleic acids were washed three times followed by magnetization, aspiration and disposal of supernatants. Afterwards, the nucleic acids were eluted by incubation of the beads with 100 μl elution buffer for 10 minutes at 70° C. with shaking. Finally, the beads were separated and the supernatant incubated with 12 μl DNase I Mix (2 μL DNase I (RNase free); 10 μl 10× DNase I buffer; Ambion/Applied Biosystems, Darmstadt, Germany) to remove contaminating DNA. After incubation for 30 minutes at 37° C., the DNA-free total RNA solution was aliquoted and stored at −80° C. or directly used for mRNA expression analysis by reverse transcription kinetic PCR (RTkPCR). All the samples were analyzed with one-step RT-kPCR for the gene expression of up to three reference genes, (RPL37A, CALM2, OAZ1) and up to 16 target genes in an ABI PRISM® 7900HT (Applied Biosystems, Darmstadt, Germany). The SuperScript® III Platinum® One-Step Quantitative RT-PCR System with ROX (6-carboxy-X-rhodamine) (Invitrogen, Karlsruhe, Germany) was used according to the manufacturer&#39;s instructions. Respective probes and primers are shown in table 1. The PCR conditions were as follows: 30 minutes at 50° C., 2 minutes at 95° C. followed by 40 cycles of 15 seconds at 95° C. and 30 seconds at 60° C. All the PCR assays were performed in triplicate. As surrogate marker for RNA yield, the housekeeper gene, RPL37A cycle threshold (Ct) value was used as described elsewhere (17). The relative gene expression levels of the target genes were calculated by the delta-Ct method using the formula: 
         [0000]      20−( Ct (target)−mean( Ct (reference genes))).
 
         [0101]    A platform transfer from Affymetrix HG_U133a arrays (fresh frozen tissue) to quantitative real time PCR (FFPE tissue) was calculated as follows. Material from 158 patients was measured using both platforms to yield paired samples. Delta-Ct values were calculated from the PCR data. Log 2-Expressions were calculated from the Affymetrix data by applying a lower bound (setting all values below the lower bound to the lower bound) and then calculating the logarithm of base 2. The application of a lower bound reduces the effect of increased relative measurement noise for low expressed genes/samples; a lower bound of 20 was used, lower bounds between 0.1 and 200 also work well. A HG_U133a probe set was selected for each PCR-measured gene by maximizing the Pearson correlation coefficient between the delta-Ct value (from PCR) and the log 2-expression (from Affymetrix). Other correlation measures will also work well, e.g. the Spearman correlation coefficient. In most cases the best-correlating probe set belonged to the intended gene, for the remaining cases the PCR-gene was removed for further processing. Those genes showing a bad correlation between platforms were also removed, where a threshold on the Pearson correlation coefficient of 0.7 was used (values of between 0.5 and 0.8) also work well. The platform transformation was finalized by calculating unsupervised z-transformations for both platforms and combining them; a single PCR-delta-Ct value then is transformed to the Affymetrix scale by the following steps: (i) apply affine linear transformation where coefficients were determined by z-transformation of PCR data, (ii) apply inverse affine linear transformation where coefficients were determined by z-transformation of Affymetrix data, (iii) invert log 2, i.e. calculate exponential with respect to base 2. Alternatives to the two-fold z-transformations are linear or higher order regression, robust regression or principal component based methods, which will also work well. 
         [0102]    The sequences of the primers and probes were as follows: 
         [0000]    
       
         
               
             
               
               
               
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Primer and probe sequences for the respective genes: 
               
             
          
           
               
                   
                   
                 Seq 
                   
                 Seq 
                   
                 Seq 
               
               
                 gene 
                 probe 
                 ID 
                 forward primer 
                 ID 
                 reverse primer 
                 ID 
               
               
                   
               
               
                 ABAT 
                 TCGCCCTAAGAGGCTCTTCCTC 
                   1 
                 GGCAACTTGAGGTCTGACTTTT 
                   2 
                 GGTCAGCTCACAAGTGGTGT 
                   3 
               
               
                   
                   
                   
                 G 
                   
                 GA 
                   
               
               
                   
               
               
                 ADRA2A 
                 TTGTCCTTTCCCCCCTCCGTGC 
                   4 
                 CCCCAAGAGCTGTTAGGTATCA 
                   5 
                 TCAATGACATGATCTCAACC 
                   6 
               
               
                   
                   
                   
                 A 
                   
                 AGAA 
                   
               
               
                   
               
               
                 APOD 
                 CATCAGCTCTCAACTCCTGGTT 
                   7 
                 ACTCACTAATGGAAAACGGAAA 
                   8 
                 TCACCTTCGATTTGATTCAC 
                   9 
               
               
                   
                 TAACA 
                   
                 GATC 
                   
                 AGTT 
                   
               
               
                   
               
               
                 ASPH 
                 TGGGAGGAAGGCAAGGTGCTCA 
                  10 
                 TGTGCCAACGAGACCAAGAC 
                  11 
                 TCGTGCTCAAAGGAGTCATC 
                  12 
               
               
                   
                 TC 
                   
                   
                   
                 A 
                   
               
               
                   
               
               
                 AURKA 
                 CCGTCAGCCTGTGCTAGGCAT 
                  13 
                 AATCTGGAGGCAAGGTTCGA 
                  14 
                 TCTGGATTTGCCTCCTGTGA 
                  15 
               
               
                   
                   
                   
                   
                   
                 A 
                   
               
               
                   
               
               
                 BIRC5 
                 AGCCAGATGACGACCCCATAGA 
                  16 
                 CCCAGTGTTTCTTCTGCTTCAA 
                  17 
                 CAACCGGACGAATGCTTTTT 
                  18 
               
               
                   
                 GGAACA 
                   
                 G 
                   
                   
                   
               
               
                   
               
               
                 CELSR2 
                 ACTGACTTTCCTTCTGGAGCAG 
                  19 
                 TCCAAGCATGTATTCCAGACTT 
                  20 
                 TGCCCACAGCCTCTTTTTCT 
                  21 
               
               
                   
                 GTGGC 
                   
                 GT 
                   
                   
                   
               
               
                   
               
               
                 CHPT1 
                 CCACGGCCACCGAAGAGGCAC 
                  22 
                 CGCTCGTGCTCATCTCCTACT 
                  23 
                 CCCAGTGCACATAAAAGGTA 
                  24 
               
               
                   
                   
                   
                   
                   
                 TGTC 
                   
               
               
                   
               
               
                 CXCL12 
                 CCACAGCAGGGTTTCAGGTTCC 
                  25 
                 GCCACTACCCCCTCCTGAA 
                  26 
                 TCACCTTGCCAACAGTTCTG 
                  27 
               
               
                   
                   
                   
                   
                   
                 AT 
                   
               
               
                   
               
               
                 CYBRD1 
                 AGGGCATCGCCATCATCGTC 
                  28 
                 GTCACCGGCTTCGTCTTCA 
                  29 
                 CAGGTCCACGGCAGTCTGT 
                  30 
               
               
                   
               
               
                 DCN 
                 TCTTTTCAGCAACCCGGTCCA 
                  31 
                 AAGGCTTCTTATTCGGGTGTGA 
                  32 
                 TGGATGGCTGTATCTCCCAG 
                  33 
               
               
                   
                   
                   
                   
                   
                 TA 
                   
               
               
                   
               
               
                 DHCR7 
                 TGAGCGCCCACCCTCTCGA 
                  34 
                 GGGCTCTGCTTCCCGATT 
                  35 
                 AGTCATAGGGCAAGCAGAAA 
                  36 
               
               
                   
                   
                   
                   
                   
                 ATTC 
                   
               
               
                   
               
               
                 E2F8 
                 CAGGATACCTAATCCCTCTCAC 
                  37 
                 AAATGTCTCCGCAACCTTGTTC 
                  38 
                 CTGCCCCCAGGGATGAG 
                  39 
               
               
                   
                 GCAG 
                   
                   
                   
                   
                   
               
               
                   
               
               
                 EPHX2 
                 TGAAGCGGGAGGACTTTTTGTA 
                  40 
                 CGATGAGAGTGTTTTATCCATG 
                  41 
                 GCTGAGGCTGGGCTCTTCT 
                  42 
               
               
                   
                 AA 
                   
                 CA 
                   
                   
                   
               
               
                   
               
               
                 ESR1 
                 ATGCCCTTTTGCCGATGCA 
                  43 
                 GCCAAATTGTGTTTGATGGATT 
                  44 
                 GACAAAACCGAGTCACATCA 
                  45 
               
               
                   
                   
                   
                 AA 
                   
                 GTAATAG 
                   
               
               
                   
               
               
                 GJA1 
                 TGCACAGCCTTTTGATTTCCCC 
                  46 
                 CGGGAAGCACCATCTCTAACTC 
                  47 
                 TTCATGTCCAGCAGCTAGTT 
                  48 
               
               
                   
                 GAT 
                   
                   
                   
                 TTTT 
                   
               
               
                   
               
               
                 HSPA2 
                 CAAGTCAGCAAACACGCAAAA 
                  49 
                 CATGCACGAACTAATCAAAAAT 
                  50 
                 ACATTATTCGAGGTTTCTCT 
                  51 
               
               
                   
                   
                   
                 GC 
                   
                 TTAATGC 
                   
               
               
                   
               
               
                 IL6ST 
                 CAAGCTCCACCTTCCAAAGGAC 
                  52 
                 CCCTGAATCCATAAAGGCATAC 
                  53 
                 CAGCTTCGTTTTTCCCTACT 
                  54 
               
               
                   
                 CT 
                   
                 C 
                   
                 TTTT 
                   
               
               
                   
               
               
                 INPP4B 
                 TCCGAGCGCTGGATTGCATGAG 
                  55 
                 GCACCAGTTACACAAGGACTTC 
                  56 
                 TCTCTATGCGGCATCCTTCT 
                  57 
               
               
                   
                   
                   
                 TTT 
                   
                 C 
                   
               
               
                   
               
               
                 MAPT 
                 AGACTATTTGCACACTGCCGCC 
                  58 
                 GTGGCTCAAAGGATAATATCAA 
                  59 
                 ACCTTGCTCAGGTCAACTGG 
                  60 
               
               
                   
                 T 
                   
                 ACAC 
                   
                 TT 
                   
               
               
                   
               
               
                 MGP 
                 CCTTCATATCCCCTCAGCAGAG 
                  61 
                 CCTTCATTAACAGGAGAAATGC 
                  62 
                 ATTGAGCTCGTGGACAGGCT 
                  63 
               
               
                   
                 ATGG 
                   
                 AA 
                   
                 TA 
                   
               
               
                   
               
               
                 NEK2 
                 TCCTGAACAAATGAATCGCATG 
                  64 
                 ATTTGTTGGCACACCTTATTAC 
                  65 
                 AAGCAGCCCAATGACCAGAT 
                  66 
               
               
                   
                 TCCTACAA 
                   
                 ATGT 
                   
                 a 
                   
               
               
                   
               
               
                 PCNA 
                 AAATACTAAAATGCGCCGGCAA 
                  67 
                 GGGCGTGAACCTCACCAGTA 
                  68 
                 CTTCGGCCCTTAGTGTAATG 
                  69 
               
               
                   
                 TGA 
                   
                   
                   
                 ATATC 
                   
               
               
                   
               
               
                 PGR 
                 TTGATAGAAACGCTGTGAGCTC 
                  70 
                 AGCTCATCAAGGCAATTGGTTT 
                  71 
                 ACAAGATCATGCAAGTTATC 
                  72 
               
               
                   
                 GA 
                   
                   
                   
                 AAGAAGTT 
                   
               
               
                   
               
               
                 PIP 
                 TGCATGGTGGTTAAAACTTACC 
                  73 
                 TGCTTGCAGTTCAAACAGAATT 
                  74 
                 CACCTTGTAGAGGGATGCTG 
                  75 
               
               
                   
                 TCA 
                   
                 G 
                   
                 CTA 
                   
               
               
                   
               
               
                 PLAT 
                 CAGAAAGTGGCCATGCCACCCT 
                  76 
                 TGGGAAGACATGAATGCACACT 
                  77 
                 GGAGGTTGGGCTTTAGCTGA 
                  78 
               
               
                   
                 G 
                   
                 A 
                   
                 A 
                   
               
               
                   
               
               
                 PRSS16 
                 CACTGCCGGTCACCCACACCA 
                  79 
                 CTGAGGAGCACAGAACCTCAAC 
                  80 
                 CGAACTCGGTACATGTCTGA 
                  81 
               
               
                   
                   
                   
                 T 
                   
                 TACAA 
                   
               
               
                   
               
               
                 PTGER3 
                 TCGGTCTGCTGGTCTCCGCTCC 
                  82 
                 CTGATTGAAGATCATTTTCAAC 
                  83 
                 GACGGCCATTCAGCTTATGG 
                  84 
               
               
                   
                   
                   
                 ATCA 
                   
                   
                   
               
               
                   
               
               
                 PTPRT 
                 TTGGCTTCTGGACACCCTCACA 
                  85 
                 GAGTTGTGGCCTCTACCATTGC 
                  86 
                 GAGCGGGAACCTTGGGATAG 
                  87 
               
               
                   
               
               
                 RACGAP1 
                 ACTGAGAATCTCCACCCGGCGC 
                  88 
                 TCGCCAACTGGATAAATTGGA 
                  89 
                 GAATGTGCGGAATCTGTTTG 
                  90 
               
               
                   
                 A 
                   
                   
                   
                 AG 
                   
               
               
                   
               
               
                 RBBP8 
                 ACCGATTCCGCTACATTCCACC 
                  91 
                 AGAAATTGGCTTCCTGCTCAAG 
                  92 
                 AAAACCAACTTCCCAAAAAT 
                  93 
               
               
                   
                 CAAC 
                   
                   
                   
                 TCTCT 
                   
               
               
                   
               
               
                 SCUBE2 
                 CTAGAGGGTTCCAGGTCCCATA 
                  94 
                 TGTGGATTCAGTTCAAGTCCAA 
                  95 
                 CCATCTCGAACTATGTCTTC 
                  96 
               
               
                   
                 CGTGACATA 
                   
                 TG 
                   
                 AATGAGT 
                   
               
               
                   
               
               
                 SEC14L2 
                 TGGGAGGCATGCAACGCGTG 
                  97 
                 AGGTCTTACTAAGCAGTCCCAT 
                  98 
                 CGACCGGCACCTGAACTC 
                  99 
               
               
                   
                   
                   
                 CTCT 
                   
                   
                   
               
               
                   
               
               
                 SQLE 
                 TATGCGTCTCCCAAAAGAAGAA 
                 100 
                 GCAAGCTTCCTTCCTCCTTCA 
                 101 
                 CCTTTAGCAGTTTTCTCCAT 
                 102 
               
               
                   
                 CACCTCG 
                   
                   
                   
                 AGTTTTATATC 
                   
               
               
                   
               
               
                 TFAP2B 
                 CAACACCACCACTAACAGGCAC 
                 103 
                 GGCATGGACAAGATGTTCTTGA 
                 104 
                 CCTCCTTGTCGCCAGTTTTA 
                 105 
               
               
                   
                 ACGTC 
                   
                   
                   
                 CT 
                   
               
               
                   
               
               
                 TOP2A 
                 CAGATCAGGACCAAGATGGTTC 
                 106 
                 CATTGAAGACGCTTCGTTATGG 
                 107 
                 CCAGTTGTGATGGATAAAAT 
                 108 
               
               
                   
                 CCACAT 
                   
                   
                   
                 TAATCAG 
                   
               
               
                   
               
               
                 TRIM29 
                 TGCTGTCTCACTACCGGCCATT 
                 109 
                 TGGAAATCTGGCAAGCAGACT 
                 110 
                 CAATCCCGTTGCCTTTGTTG 
                 111 
               
               
                   
                 CTACG 
                   
                   
                   
                   
                   
               
               
                   
               
               
                 UBE2C 
                 TGAACACACATGCTGCCGAGCT 
                 112 
                 CTTCTAGGAGAACCCAACATTG 
                 113 
                 GTTTCTTGCAGGTACTTCTT 
                 114 
               
               
                   
                 CTG 
                   
                 ATAGT 
                   
                 AAAAGCT 
                   
               
               
                   
               
               
                 WNT5A 
                 TATTCACATCCCCTCAGTTGCA 
                 115 
                 CTGTGGCTCTTAATTTATTGCA 
                 116 
                 TTAGTGCTTTTTGCTTTCAA 
                 117 
               
               
                   
                 GTGAATTG 
                   
                 TAATG 
                   
                 GATCTT 
                   
               
               
                   
               
               
                 STC2 
                 TCTCACCTTGACCCTCAGCCAA 
                 118 
                 ACATTTGACAAATTTCCCTTAG 
                 119 
                 CCAGGACGCAGCTTTACCAA 
                 120 
               
               
                   
                 G 
                   
                 GATT 
                   
                   
                   
               
               
                   
               
               
                 AZGP1 
                 CACCAGCCACCAGGCCCCAG 
                 121 
                 TCCTGGACCGGCAAGATC 
                 122 
                 TAGGCCAGGCACTTCAGTTT 
                 123 
               
               
                   
                   
                   
                   
                   
                 C 
                   
               
               
                   
               
               
                 CALM2 
                 TCGCGTCTCGGAAACCGGTAGC 
                 124 
                 GAGCGAGCTGAGTGGTTGTG 
                 125 
                 AGTCAGTTGGTCAGCCATGC 
                 126 
               
               
                   
                   
                   
                   
                   
                 T 
                   
               
               
                   
               
               
                 CDH1 
                 CCTGCCAATCCCGATGAAATTG 
                 127 
                 TGAGTGTCCCCCGGTATCTTC 
                 128 
                 TCAGCCGCTTTCAGATTTTC 
                 129 
               
               
                   
                 GAAAT 
                   
                   
                   
                 A 
                   
               
               
                   
               
               
                 NMU 
                 ACCCTGCTGACCTTCTTCCATT 
                 130 
                 AGAAATTGGCTTCCTGCTCAAG 
                 131 
                 AAAACCAACTTCCCAAAAAT 
                 132 
               
               
                   
                 CCGT 
                   
                   
                   
                 TCTCT 
                   
               
               
                   
               
               
                 OAZ1 
                 TGCTTCCACAAGAACCGCGAGG 
                 133 
                 CGAGCCGACCATGTCTTCAT 
                 134 
                 AAGCCCAAAAAGCTGAAGGT 
                 135 
               
               
                   
                 A 
                   
                   
                   
                 T 
                   
               
               
                   
               
               
                 PVALB 
                 AAGTTCTTCCAAATGGTCGGCC 
                 136 
                 CCGACTCCTTCGACCACAA 
                 137 
                 CATCATCCGCACTCTTTTTC 
                 138 
               
               
                   
                   
                   
                   
                   
                 TTC 
                   
               
               
                   
               
               
                 RPL37A 
                 TGGCTGGCGGTGCCTGGA 
                 139 
                 TGTGGTTCCTGCATGAAGACA 
                 140 
                 GTGACAGCGGAAGTGGTATT 
                 141 
               
               
                   
                   
                   
                   
                   
                 GTAC 
               
               
                   
               
             
          
         
       
     
         [0103]    Table 2, below, lists the genes used in the methods of the invention and in the particular embodiments T5, T1, T4, and T5b. Table 2 also shows whether overexpression of a given gene is indicative of good or bad outcome under Tamoxifen therapy, Table 2 lists the function of the gene, the compartment localization within the cell and the cellular processes it is involved in. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 List of genes of algorithms T5, T1, T4, and T5b: 
               
             
          
           
               
                   
                   
                 High 
                   
                   
                   
               
               
                 Gene 
                 Name 
                 Expression 
                 Function 
                 Component 
                 Process 
               
               
                   
               
               
                 UBE2C 
                 ubiquitin- 
                 Bad 
                 ATP 
                 cytosol 
                 cell 
               
               
                   
                 conjugating 
                 Outcome 
                 binding 
                   
                 division 
               
               
                   
                 enzyme E2C 
               
               
                 BIRC5 
                 baculoviral 
                 Bad 
                 Ran GTPase 
                 cytosol 
                 cell cycle 
               
               
                   
                 IAP repeat- 
                 Outcome 
                 binding 
               
               
                   
                 containing 5 
               
               
                 DHCR7 
                 7- 
                 Bad 
                 7- 
                 endoplasmatic 
                 regulation 
               
               
                   
                 dehydrocholesterol 
                 Outcome 
                 dehydrocholesterol 
                 reticulum 
                 of cell 
               
               
                   
                 reductase 
                   
                 reductase 
                 membrane 
                 proliferation 
               
               
                   
                   
                   
                 activity 
               
               
                 RACGAP1 
                 Rac GTPase 
                 Bad 
                 GTPase 
                 cytoplasm 
                 cell cycle 
               
               
                   
                 activating 
                 Outcome 
                 activator 
               
               
                   
                 protein 1 
                   
                 activity 
               
               
                 AURKA 
                 aurora 
                 Bad 
                 ATP 
                 centrosome 
                 mitotic 
               
               
                   
                 kinase A 
                 Outcome 
                 binding 
                   
                 cell cycle 
               
               
                 PVALB 
                 parvalbumin 
                 Bad 
                 calcium 
               
               
                   
                   
                 Outcome 
                 ion 
               
               
                   
                   
                   
                 binding 
               
               
                 NMU 
                 neuromedin U 
                 Bad 
                 receptor 
                 extracellular 
                 signal 
               
               
                   
                   
                 Outcome 
                 binding 
                 region 
                 trans- 
               
               
                   
                   
                   
                   
                   
                 duction 
               
               
                 STC2 
                 stanniocalcin 2 
                 Good 
                 hormone 
                 extracellular 
                 cell 
               
               
                   
                   
                 Outcome 
                 activity 
                 region 
                 surface 
               
               
                   
                   
                   
                   
                   
                 receptor 
               
               
                   
                   
                   
                   
                   
                 linked 
               
               
                   
                   
                   
                   
                   
                 signal 
               
               
                   
                   
                   
                   
                   
                 trans- 
               
               
                   
                   
                   
                   
                   
                 duction 
               
               
                 AZGP1 
                 alpha-2- 
                 Good 
                 protein 
                 extracellular 
                 negative 
               
               
                   
                 glycoprotein 1 
                 Outcome 
                 transmembrane 
                 region 
                 regulation 
               
               
                   
                   
                   
                 transporter 
                   
                 of cell 
               
               
                   
                   
                   
                 activity 
                   
                 proliferation 
               
               
                 RBBP8 
                 retinoblastoma 
                 Good 
                 protein 
                 nucleus 
                 cell cycle 
               
               
                   
                 binding 
                 Outcome 
                 binding 
                   
                 checkpoint 
               
               
                   
                 protein 8 
               
               
                 IL6ST 
                 interleukin 
                 Good 
                 receptor 
                 extracellular 
                 signal 
               
               
                   
                 6 signal 
                 Outcome 
                 activity 
                 region 
                 trans- 
               
               
                   
                 transducer 
                   
                   
                   
                 duction 
               
               
                 MGP 
                 matrix Gla 
                 Good 
                 extracellular 
                 extracellular 
                 cell 
               
               
                   
                 protein 
                 Outcome 
                 matrix 
                 region 
                 differentiation 
               
               
                   
                   
                   
                 structural 
               
               
                   
                   
                   
                 constituent 
               
               
                 PTGER3 
                 prostagland 
                 Good 
                 ligand- 
                 plasma 
                 signal 
               
               
                   
                 in E 
                 Outcome 
                 dependent 
                 membrane 
                 trans- 
               
               
                   
                 receptor 3 
                   
                 receptor 
                   
                 duction 
               
               
                   
                   
                   
                 activity 
               
               
                 CXCL12 
                 chemokine 
                 Good 
                 chemokine 
                 extracellular 
                 signal 
               
               
                   
                 (C-XC 
                 Outcome 
                 activity 
                 region 
                 trans- 
               
               
                   
                 motif) 
                   
                   
                   
                 duction 
               
               
                   
                 ligand 12 
               
               
                 ABAT 
                 4- 
                 Good 
                 transferase 
                 mitochondrion 
                 gamma- 
               
               
                   
                 aminobutyrate 
                 Outcome 
                 activity 
                   
                 aminobutyric 
               
               
                   
                 aminotransferase 
                   
                   
                   
                 acid 
               
               
                   
                   
                   
                   
                   
                 catabolic 
               
               
                   
                   
                   
                   
                   
                 process 
               
               
                 CDH1 
                 cadherin 1 
                 Good 
                 cell 
                 plasma 
                 homophilic 
               
               
                   
                   
                 Outcome 
                 adhesion 
                 membrane 
                 cell 
               
               
                   
                   
                   
                 molecule 
                   
                 adhesion 
               
               
                   
                   
                   
                 binding 
               
               
                 PIP 
                 prolactin- 
                 Good 
                 actin 
                 extracellular 
               
               
                   
                 induced 
                 Outcome 
                 bindin 
                 region 
               
               
                   
                 protein 
               
               
                 CALM2 
                   
                   
                   
                   
                 Reference 
               
               
                   
                   
                   
                   
                   
                 Gene 
               
               
                 OAZ1 
                   
                   
                   
                   
                 Reference 
               
               
                   
                   
                   
                   
                   
                 Gene 
               
               
                 RPL37A 
                   
                   
                   
                   
                 Reference 
               
               
                   
                   
                   
                   
                   
                 Gene 
               
               
                   
               
             
          
         
       
     
         [0104]    Table 3, below, shows the combinations of genes used for each algorithm. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Combination of genes for the respective algorithms: 
               
             
          
           
               
                 Gene 
                 Algo_T1 
                 Algo_T4 
                 Algo_T5 
                 Algo_T5b 
               
               
                   
               
               
                 UBE2C 
                   
                   
                 X 
                   
               
               
                 BIRC5 
                 X 
                 X 
                 X 
               
               
                 DHCR7 
                   
                 X 
                 X 
                 X 
               
               
                 RACGAP1 
                   
                 X 
                   
                 X 
               
               
                 AURKA 
                 X 
               
               
                 PVALB 
                 X 
                 X 
               
               
                 NMU 
                 X 
                   
                   
                 X 
               
               
                 STC2 
                 X 
                 X 
                 X 
               
               
                 AZGP1 
                   
                   
                 X 
                 X 
               
               
                 RBBP8 
                 X 
                   
                 X 
                 X 
               
               
                 IL6ST 
                   
                 X 
                 X 
                 X 
               
               
                 MGP 
                   
                   
                 X 
                 X 
               
               
                 PTGER3 
                 X 
                 X 
               
               
                 CXCL12 
                 X 
                 X 
               
               
                 ABAT 
                   
                 X 
               
               
                 CDH1 
                 X 
               
               
                 PIP 
                 X 
               
               
                   
               
             
          
         
       
     
         [0105]    Table 4, below, shows Affy probeset ID and TaqMan design ID mapping of the marker genes of the present invention. 
         [0000]    
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 Gene symbol, Affy probeset ID and TaqMan design ID mapping: 
               
             
          
           
               
                   
                 Gene 
                 Design ID 
                 Probeset ID 
               
               
                   
                   
               
               
                   
                 UBE2C 
                 R65 
                 202954_at 
               
               
                   
                 BIRC5 
                 SC089 
                 202095_s_at 
               
               
                   
                 DHCR7 
                 CAGMC334 
                 201791_s_at 
               
               
                   
                 RACGAP1 
                 R125-2 
                 222077_s_at 
               
               
                   
                 AURKA 
                 CAGMC336 
                 204092_s_at 
               
               
                   
                 PVALB 
                 CAGMC339 
                 205336_at 
               
               
                   
                 NMU 
                 CAGMC331 
                 206023_at 
               
               
                   
                 STC2 
                 R52 
                 203438_at 
               
               
                   
                 AZGP1 
                 CAGMC372 
                 209309_at 
               
               
                   
                 RBBP8 
                 CAGMC347 
                 203344_s_at 
               
               
                   
                 IL6ST 
                 CAGMC312 
                 212196_at 
               
               
                   
                 MGP 
                 CAGMC383 
                 202291_s_at 
               
               
                   
                 PTGER3 
                 CAGMC315 
                 213933_at 
               
               
                   
                 CXCL12 
                 CAGMC342 
                 209687_at 
               
               
                   
                 ABAT 
                 CAGMC338 
                 209460_at 
               
               
                   
                 CDH1 
                 CAGMC335 
                 201131_s_at 
               
               
                   
                   
               
             
          
         
       
     
         [0106]    Table 5, below, shows full names, Entrez GeneID, gene bank accession number and chromosomal location of the marker genes of the present invention 
         [0000]    
       
         
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                   
               
               
                 Official 
                   
                 Entrez 
                 Accesion 
                   
               
               
                 Symbol 
                 Official Full Name 
                 GeneID 
                 Number 
                 Location 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 UBE2C 
                 ubiquitin- 
                 11065 
                 U73379 
                 20q13.12 
               
               
                   
                 conjugating enzyme 
               
               
                   
                 E2C 
               
               
                 BIRC5 
                 baculoviral IAP 
                 332 
                 U75285 
                 17q25 
               
               
                   
                 repeat-containing 5 
               
               
                 DHCR7 
                 7- 
                 1717 
                 AF034544 
                 11q13.4 
               
               
                   
                 dehydrocholesterol 
               
               
                   
                 reductase 
               
               
                 STC2 
                 staniocalcin 2 
                 8614 
                 AB012664 
                 5q35.2 
               
               
                 RBBP8 
                 retinoblastoma 
                 5932 
                 AF043431 
                 18q11.2 
               
               
                   
                 binding protein 8 
               
               
                 IL6ST 
                 interleukin 6 
                 3572 
                 M57230 
                 5q11 
               
               
                   
                 signal transducer 
               
               
                 MGP 
                 matrix Gla protein 
                 4256 
                 M58549 
                 12p12.3 
               
               
                 AZGP1 
                 alpha-2- 
                 563 
                 BC005306 
                 11q22.1 
               
               
                   
                 glycoprotein 1, 
               
               
                   
                 zinc-binding 
               
               
                 RACGAP1 
                 Rac GTPase 
                 29127 
                 NM_013277 
                 12q13 
               
               
                   
                 activating protein 1 
               
               
                 AURKA 
                 aurora kinase A 
                 6790 
                 BC001280 
                 20q13 
               
               
                 PVALB 
                 parvalbumin 
                 5816 
                 NM_002854 
                 22q13.1 
               
               
                 NMU 
                 neuromedin U 
                 10874 
                 X76029 
                 4q12 
               
               
                 PTGER3 
                 prostaglandin E 
                 5733 
                 X83863 
                 1p31.2 
               
               
                   
                 receptor 3 (subtype 
               
               
                   
                 EP3) 
               
               
                 CXCL12 
                 chemokine (C-X-C 
                 6387 
                 L36033 
                 10q11.1 
               
               
                   
                 motif) ligand 12 
               
               
                   
                 (stromal cell- 
               
               
                   
                 derived factor 1) 
               
               
                 ABAT 
                 4-aminobutyrat 
                 18 
                 L32961 
                 16p13.2 
               
               
                   
                 aminotransferase 
               
               
                 CDH1 
                 cadherin 1, type 1, 
                 999 
                 L08599 
                 16q22.1 
               
               
                   
                 E-cadherin 
               
               
                   
                 (epithelial) 
               
               
                 PIP 
                 prolactin-induced 
                 5304 
                 NMM_002652 
                 7q32- 
               
               
                   
                 protein 
                   
                   
                 qter 
               
               
                   
               
             
          
         
       
     
         [0107]    Example Algorithm T5: 
         [0108]    Algorithm T5 is a committee of four members where each member is a linear combination of two genes. The mathematical formulas for T5 are shown below; the notation is the same as for T1. T5 can be calculated from gene expression data only.
   riskMember1=0.434039 [0.301 . . . 0.567]*(0.939*BIRC5 −3.831)
       −0.491845 [−0.714 . . . −0.270]*(0.707*RBBP8 −0.934)   
       riskMember2=0.488785 [0.302 . . . 0.675]*(0.794*UBE2C −1.416)
       −0.374702 [−0.570 . . . −0.179]*(0.814*IL6ST −5.034)   
       riskMember3=−0.39169 [−0.541 . . . −0.242]*(0.674*AZGP1 −0.777)
       +0.44229 [0.256 . . . 0.628]*(0.891*DHCR7 −4.378)   
       riskMember4=−0.377752 [−0.543 . . . −0.212]*(0.485*MGP +4.330)
       −0.177669 [−0.267 . . . −0.088]*(0.826*STC2 −3.630)   
       risk=riskMember1+riskMember2+riskMember3+riskMember4   
 
         [0118]    Coefficients on the left of each line were calculated as COX proportional hazards regression coefficients, the numbers in squared brackets denote 95% confidence bounds for these coefficients. In other words, instead of multiplying the term (0.939*BIRC5 −3.831) with 0.434039, it may be multiplied with any coefficient between 0.301 and 0.567 and still give a predictive result with in the 95% confidence bounds. Terms in round brackets on the right of each line denote a platform transfer from PCR to Affymetrix: The variables PVALB, CDH1, . . . denote PCR-based expressions normalized by the reference genes (delta-Ct values), the whole term within round brackets corresponds to the logarithm (base 2) of Affymetrix microarray expression values of corresponding probe sets. 
         [0119]    Performance of the algorithm T5 was tested in Tamoxifen or Anastrozole treated patients with no more than 3 positive lymph nodes and ER+, HER2-tumors, who participated in the randomized clinical trials ABCSG06 (n=332) or ABCSG08 (n=1244). As shown in  FIG. 1 , Cox regression analysis reveals, that the T5 score has a significant association with the development of distant metastasis in all cohorts tested. 
         [0120]    Kaplan Meier analysis was performed, after classifying the patients of the combined ABCSG cohorts using a predefined cut off for T5 score. Patients with a low risk of development of a distant metastasis had a T5 score ≦−9.3, while patients with a high risk of development of a distant metastasis had a T5 score above −9.3. As shown in  FIG. 2 , a highly significant separation of both risk groups is observed. 
         [0121]    Importantly, the T5 score was evaluated and compared against “Adjuvant!Online”, an online tool to aid in therapy selection based on entry of clinical parameters such as tumor size, tumor grade and nodal status. When the T5 score was tested by bivariate Cox regression against the Adjuvant!Online Relapse Risk score, both scores remained a significant association with the development of distant metastasis. Bivariate Cox regression using dichotomized data, which were stratified according to T5 (cut off −9.3) respectively to Adjuvant!Online (cut off 8), again yielded highly significant and independent associations with time to metastasis as clinical endpoint. 
         [0000]    
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 6 
               
             
             
               
                   
               
               
                 Bivariate Cox regression von T5 und Adjuvant!Online 
               
             
          
           
               
                 Variable 
                 Hazard ratio 
                 95% CI* 
                 P 
               
               
                   
               
               
                 Adjuvant!Online 
                 2.36 
                 1.58-3.54 
                 &lt;0.0001 
               
               
                 Gene-expression 
                 2.62 
                 1.71-4.01 
                 &lt;0.0001 
               
               
                 signature (risk group) 
               
               
                 Adjuvant!Online (score) 
                 1.04 
                 1.02-1.06 
                 &lt;0.0001 
               
               
                 Gene-expression 
                 1.35 
                 1.21-1.49 
                 &lt;0.0001 
               
               
                 signature (risk group) 
               
               
                   
               
               
                 with HR = Hazard Ratio, 95% CI = 95% Confidence Interval, p = P value. 
               
             
          
         
       
     
         [0122]    Exemplary Kaplan Meyer Curves are shown in  FIG. 1  wherein High=High Risk Group, Low=Low Risk Group according to a predefined cut off 
         [0123]    A high value of the T5 score indicates an increased risk of occurrence of distant metastasis in a given time period. 
         [0124]    This has been shown to be the case for patients having been treated with tamoxifen and also for patients having been treated with aromatase inhibitors. 
         [0125]    Example Algorithm T1: 
         [0126]    Algorithm T1 is a committee of three members where each member is a linear combination of up to four variables. In general variables may be gene expressions or clinical variables. In T1 the only non-gene variable is the nodal status coded 0, if patient is lymph-node negative and 1, if patient is lymph-node-positive. The mathematical formulas for T1 are shown below.
   riskMember1=+0.193935 [0.108 . . . 0.280]*(0.792*PVALB −2.189)
       −0.240252 [−0.400 . . . −0.080]*(0.859*CDH1 −2.900)   −0.270069 [−0.385 . . . −0.155]*(0.821*STC2 −3.529)   +1.2053 [0.534−1.877]*nodalStatus   
       riskMember2=−0.25051 [−0.437 . . . −0.064]*(0.558*CXCL12 +0.324)
       −0.421992 [−0.687 . . . −0.157]*(0.715*RBBP8 −1.063)   +0.148497 [0.029 . . . 0.268]*(1.823*NMU −12.563)   +0.293563 [0.108 . . . 0.479]*(0.989*BIRC5 −4.536)   
       riskMember3=+0.308391 [0.074 . . . 0.543]*(0.812*AURKA −2.656)
       −0.225358 [−0.395 . . . −0.055]*(0.637*PTGER3 +0.492)   −0.116312 [−0.202 . . . −0.031]*(0.724*PIP +0.985)   
       risk=+riskMember1+riskMember2+riskMember3   
 
         [0139]    Coefficients on the left of each line were calculated as COX proportional hazards regression coefficients, the numbers in squared brackets denote 95% confidence bounds for these coefficients. Terms in round brackets on the right of each line denote a platform transfer from PCR to Affymetrix: The variables PVALB, CDH1, . . . denote PCR-based expressions normalized by the reference genes, the whole term within round brackets corresponds to the logarithm (base 2) of Affymetrix microarray expression values of corresponding probe sets. 
         [0140]    Example Algorithm T4: 
         [0141]    Algorithm T4 is a linear combination of motifs. The top 10 genes of several analyses of Affymetrix datasets and PCR data were clustered to motifs. Genes not belonging to a cluster were used as single gene-motifs. COX proportional hazards regression coefficients were found in a multivariate analysis. 
         [0142]    In general motifs may be single gene expressions or mean gene expressions of correlated genes. The mathematical formulas for T4 are shown below.
   prolif=((0.84 [0.697 . . . 0.977]*RACGAP1 −2.174)+(0.85 [0.713 . . . 0.988]*DHCR7 −3.808)+(0.94 [0.786 . . . 1.089]*BIRC5 −3.734))/3   motiv2=((0.83 [0.693 . . . 0.96]*IL6ST −5.295)+(1.11 [0.930 . . . 1.288]*ABAT −7.019)+(0.84 [0.701 . . . 0.972]*STC2 −3.857))/3   ptger3=(PTGER3*0.57 [0.475 . . . 0.659]+1.436)   cxcl12=(CXCL12*0.53 [0.446 . . . 0.618]+0.847)   pvalb=(PVALB*0.67 [0.558 . . . 0.774]−0.466)   
 
         [0148]    Factors and offsets for each gene denote a platform transfer from PCR to Affymetrix: The variables RACGAP1, DHCR7, . . . denote PCR-based expressions normalized by CALM2 and PPIA, the whole term within round brackets corresponds to the logarithm (base 2) of Affymetrix microarray expression values of corresponding probe sets. 
         [0149]    The numbers in squared brackets denote 95% confidence bounds for these factors. 
         [0150]    As the algorithm performed even better in combination with a clinical variable the nodal status was added. In T4 the nodal status is coded 0, if patient is lymph-node negative and 1, if patient is lymph-node-positive. With this, algorithm T4 is:
   risk=−0.32 [−0.510 . . . −0.137]*motiv2   +0.65 [0.411 . . . 0.886]*prolif   0.24 [−0.398 . . . −0.08]*ptger3   0.05 [−0.225 . . . 0.131]*cxcl12   +0.09 [0.019 . . . 0.154]*pvalb   +nodalStatus   
 
         [0157]    Coefficients of the risk were calculated as COX proportional hazards regression coefficients, the numbers in squared brackets denote 95% confidence bounds for these coefficients. 
         [0158]    Algorithm T5b is a committee of two members where each member is a linear combination of four genes. The mathematical formulas for T5b are shown below, the notation is the same as for T1 and T5. In T5b a non-gene variable is the nodal status coded 0, if patient is lymph-node negative and 1, if patient is lymph-node-positive and 0.5 if the lymph-node status is unknown. T5b is defined by:
   riskMember1=0.359536 [0.153 . . . 0.566]*(0.891*DHCR7 −4.378)
       −0.288119 [−0.463 . . . −0.113]*(0.485*MGP +4.330)   +0.257341 [0.112 . . . 0.403]*(1.118*NMU −5.128)   −0.337663 [−0.499 . . . −0.176]*(0.674*AZGP1 −0.777)   
       riskMember2=−0.374940 [−0.611 . . . −0.139]*(0.707*RBBP8 −0.934)
       −0.387371 [−0.597 . . . −0.178]*(0.814*IL6ST −5.034)   +0.800745 [0.551 . . . 1.051]*(0.860*RACGAP1 −2.518)   +0.770650 [0.323 . . . 1.219]*Nodalstatus   
       risk=riskMember1+riskMember2   
 
         [0168]    The skilled person understands that these algorithms represent particular examples and that based on the information regarding association of gene expression with outcome as given in table 2 alternative algorithms can be established using routine skills. 
         [0169]    Algorithm Simplification by Employing Subsets of Genes 
         [0170]    “Example algorithm T5” is a committee predictor consisting of 4 members with 2 genes of interest each. Each member is an independent and self-contained predictor of distant recurrence, each additional member contributes to robustness and predictive power of the algorithm to predict time to metastasis, time to death or likelihood of survival for a breast cancer patient. The equation below shows the “Example Algorithm T5”; for ease of reading the number of digits after the decimal point has been truncated to 2; the range in square brackets lists the estimated range of the coefficients (mean+/−3 standard deviations). 
         [0171]    T5 Algorithm:
       +0.41 [0.21 . . . 0.61]*BIRC5 −0.33 [−0.57 . . . −0.09]*RBBP8   +0.38 [0.15 . . . 0.61]*UBE2C −0.30 [−0.55 . . . −0.06]*IL6ST   −0.28 [−0.43 . . . −0.12]*AZGP1+0.42 [0.16 . . . 0.68]*DHCR7   −0.18 [−0.31 . . . −0.06]*MGP −0.13 [−0.25 . . . −0.02]*STC2   c-indices: trainSet=0.724,       
 
         [0177]    Gene names in the algorithm denote the difference of the mRNA expression of the gene compared to one or more housekeeping genes as described above. 
         [0178]    Analysing a cohort different from the finding cohort (234 tumor samples) it was surprising to learn that some simplifications of the “original T5 Algorithm” still yielded a diagnostic performance not significantly inferior to the original T5 algorithm. The most straightforward simplification was reducing the committee predictor to one member only. Examples for the performance of the “one-member committees” are shown below:
   member 1 only:
       +0.41 [0.21 . . . 0.61]*BIRC5 −0.33 [−0.57 . . . −0.09]*RBBP8   c-indices: trainSet=0.653, independentCohort=0.681   
       member 2 only:
       +0.38 [0.15 . . . 0.61]*UBE2C −0.30 [−0.55 . . . −0.06]*IL6ST   c-indices: trainSet=0.664, independentCohort=0.696   
       member 3 only:
       −0.28 [−0.43 . . . −0.12]*AZGP1+0.42 [0.16 . . . 0.68]*DHCR7   c-indices: trainSet=0.666, independentCohort=0.601   
       member 4 only:
       −0.18 [−0.31 . . . −0.06]*MGP −0.13 [−0.25 . . . −0.02]*STC2   c-indices: trainSet=0.668, independentCohort=0.593   
       
 
         [0191]    The performance of the one member committees as shown in an independent cohort of 234 samples is notably reduced compared to the performance of the full algorithm. Still, using a committee consisting of fewer members allows for a simpler, less costly estimate of the risk of breast cancer recurrence or breast cancer death that might be acceptable for certain diagnostic purposes. 
         [0192]    Gradually combining more than one but less than four members to a new prognostic committee predictor algorithm, frequently leads to a small but significant increase in the diagnostic performance compared to a one-member committee. It was surprising to learn that there were marked improvements by some combination of committee members while other combinations yielded next to no improvement. Initially, the hypothesis was that a combination of members representing similar biological motives as reflected by the employed genes yielded a smaller improvement than combining members reflecting distinctly different biological motives. Still, this was not the case. No rule could be identified to foretell the combination of some genes to generate an algorithm exhibiting more prognostic power than another combination of genes. Promising combinations could only be selected based on experimental data. 
         [0193]    Identified combinations of combined committee members to yield simplified yet powerful algorithms are shown below.
   members 1 and 2 only:
       +0.41 [0.21 . . . 0.61]*BIRC5 −0.33 [−0.57 . . . −0.09]*RBBP8   +0.38 [0.15 . . . 0.61]*UBE2C −0.30 [−0.55 . . . −0.06]*IL6ST   c-indices: trainSet=0.675, independentCohort=0.712   
       members 1 and 3 only:
       +0.41 [0.21 . . . 0.61]*BIRC5 −0.33 [−0.57 . . . −0.09]*RBBP8   −0.28 [−0.43 . . . −0.12]*AZGP1+0.42 [0.16 . . . 0.68]*DHCR7   c-indices: trainSet=0.697, independentCohort=0.688   
       members 1 and 4 only:
       +0.41 [0.21 . . . 0.61]*BIRC5 −0.33 [−0.57 . . . −0.09]*RBBP8   −0.18 [−0.31 . . . −0.06]*MGP −0.13 [−0.25 . . . −0.02]*STC2   c-indices: trainSet=0.705, independentCohort=0.679   
       members 2 and 3 only:
       +0.38 [0.15 . . . 0.61]*UBE2C −0.30 [−0.55 . . . −0.06]*IL6ST   −0.28 [−0.43 . . . −0.12]*AZGP1+0.42 [0.16 . . . 0.68]*DHCR7   c-indices: trainSet=0.698, independentCohort=0.670   
       members 1, 2 and 3 only:
       +0.41 [0.21 . . . 0.61]*BIRC5 −0.33 [−0.57 . . . −0.09]*RBBP8   +0.38 [0.15 . . . 0.61]*UBE2C −0.30 [−0.55 . . . −0.06]*IL6ST   −0.28 [−0.43 . . . −0.12]*AZGP1+0.42 [0.16 . . . 0.68]*DHCR7   c-indices: trainSet=0.701, independentCohort=0.715   
       
 
         [0215]    Not omitting complete committee members but a single gene or genes from different committee members is also possible but requires a retraining of the entire algorithm. Still, it can also be advantageous to perform. The performance of simplified algorithms generated by omitting entire members or individual genes is largely identical. 
         [0216]    Algorithm Variants by Gene Replacement 
         [0217]    Described algorithms, such as “Example algorithm T5”, above can be also be modified by replacing one or more genes by one or more other genes. The purpose of such modifications is to replace genes difficult to measure on a specific platform by a gene more straightforward to assay on this platform. While such transfer may not necessarily yield an improved performance compared to a starting algorithm, it can yield the clue to implanting the prognostic algorithm to a particular diagnostic platform. In general, replacing one gene by another gene while preserving the diagnostic power of the predictive algorithm can be best accomplished by replacing one gene by a co-expressed gene with a high correlation (shown e.g. by the Pearson correlation coefficient). Still, one has to keep in mind that the mRNA expression of two genes highly correlative on one platform may appear quite independent from each other when assessed on another platform. Accordingly, such an apparently easy replacement when reduced to practice experimentally, may yield disappointingly poor results as well as surprising strong results, always depending on the imponderabilia of the platform employed. By repeating this procedure one can replace several genes. 
         [0218]    The efficiency of such an approach can be demonstrated by evaluating the predictive performance of the T5 algorithm score and its variants on the validation cohorts. The following table shows the c-index with respect to endpoint distant recurrence in two validation cohorts. 
         [0000]    
       
         
               
               
               
             
           
               
                 TABLE 7 
               
               
                   
               
               
                 Variant 
                 Validation Study A 
                 Validation Study B 
               
               
                   
               
             
             
               
                 original algorithm T5 
                 c-index = 0.718 
                 c-index = 0.686 
               
               
                 omission of BIRC5 (setting 
                 c-index = 0.672 
                 c-index = 0.643 
               
               
                 expression to some 
               
               
                 constant) 
               
               
                 replacing BIRC5 by UBE2C 
                 c-index = 0.707 
                 c-index = 0.678 
               
               
                 (no adjustment of the 
               
               
                 coefficient) 
               
               
                   
               
             
          
         
       
     
         [0219]    One can see that omission of one of the T5 genes, here shown for BIRC5 for example, notably reduces the predictive performance. Replacing it with another gene yields about the same performance. 
         [0220]    A better method of replacing a gene is to re-train the algorithm. Since T5 consists of four independent committee members one has to re-train only the member that contains the replaced gene. The following equations demonstrate replacements of genes of the T5 algorithm shown above trained in a cohort of 234 breast cancer patients. Only one member is shown below, for c-index calculation the remaining members were used unchanged from the original T5 Algorithm. The range in square brackets lists the estimated range of the coefficients: mean+/−3 standard deviations.
   Member 1 of T5:   Original member 1:
       +0.41 [0.21 . . . 0.61]*BIRC5 −0.33 [−0.57 . . . −0.09]*RBBP8   c-indices: trainSet=0.724, independentCohort=0.705   
       replace BIRC5 by TOP2A in member 1:
       +0.47 [0.24 . . . 0.69]*TOP2A −0.34 [−0.58 . . . −0.10]*RBBP8   c-indices: trainSet=0.734, independentCohort=0.694   
       replace BIRC5 by RACGAP1 in member 1:
       +0.69 [0.37 . . . 1.00]*RACGAP1 −0.33 [−0.57 . . . −0.09]*RBBP8   c-indices: trainSet=0.736, independentCohort=0.743   
       replace RBBP8 by CELSR2 in member 1:
       +0.38 [0.19 . . . 0.57]*BIRC5 −0.18 [−0.41 . . . 0.05]*CELSR2   c-indices: trainSet=0.726, independentCohort=0.680   
       replace RBBP8 by PGR in member 1:
       +0.35 [0.15 . . . 0.54]*BIRC5 −0.09 [−0.23 . . . 0.05]*PGR   c-indices: trainSet=0.727, independentCohort=0.731   
       Member 2 of T5:   Original member 2:
       +0.38 [0.15 . . . 0.61]*UBE2C −0.30 [−0.55 . . . −0.06]*IL6ST   c-indices: trainSet=0.724, independentCohort=0.725   
       replace UBE2C by RACGAP1 in member 2:
       +0.65 [0.33 . . . 0.96]*RACGAP1 −0.38 [−0.62 . . . −0.13]*IL6ST   c-indices: trainSet=0.735, independentCohort=0.718   
       replace UBE2C by TOP2A in member 2:
       +0.42 [0.20 . . . 0.65]*TOP2A −0.38 [−0.62 . . . −0.13]*IL6ST   c-indices: trainSet=0.734, independentCohort=0.700   
       replace IL6ST by INPP4B in member 2:
       +0.40 [0.17 . . . 0.62]*UBE2C −0.25 [−0.55 . . . 0.05]*INPP4B   c-indices: trainSet=0.725, independentCohort=0.686   
       replace IL6ST by MAPT in member 2:
       +0.45 [0.22 . . . 0.69]*UBE2C −0.14 [−0.28 . . . 0.01]*MAPT   c-indices: trainSet=0.727, independentCohort=0.711   
       Member 3 of T5:   Original member 3:
       −0.28 [−0.43 . . . −0.12]*AZGP1+0.42 [0.16 . . . 0.68]*DHCR7   c-indices: trainSet=0.724, independentCohort=0.705   
       replace AZGP1 by PIP in member 3:
       −0.10 [−0.18 . . . −0.02]*PIP+0.43 [0.16 . . . 0.70]*DHCR7   c-indices: trainSet=0.725, independentCohort=0.692   
       replace AZGP1 by EPHX2 in member 3:
       −0.23 [−0.43 . . . −0.02]*EPHX2+0.37 [0.10 . . . 0.64]*DHCR7   c-indices: trainSet=0.719, independentCohort=0.698   
       replace AZGP1 by PLAT in member 3:
       −0.23 [−0.40 . . . −0.06]*PLAT+0.43 [0.18 . . . 0.68]*DHCR7   c-indices: trainSet=0.712, independentCohort=0.715   
       replace DHCR7 by AURKA in member 3:
       −0.23 [−0.39 . . . −0.06]*AZGP1+0.34 [0.10 . . . 0.58]*AURKA   c-indices: trainSet=0.716, independentCohort=0.733   
       Member 4 of T5:   Original member 4:
       −0.18 [−0.31 . . . −0.06]*MGP −0.13 [−0.25 . . . −0.02]*STC2   c-indices: trainSet=0.724, independentCohort=0.705   
       replace MGP by APOD in member 4:
       −0.16 [−0.30 . . . −0.03]*APOD −0.14 [−0.26 . . . −0.03]*STC2   c-indices: trainSet=0.717, independentCohort=0.679   
       replace MGP by EGFR in member 4:
       −0.21 [−0.37 . . . −0.05]*EGFR −0.14 [−0.26 . . . −0.03]*STC2   c-indices: trainSet=0.715, independentCohort=0.708   
       replace STC2 by INPP4B in member 4:
       −0.18 [−0.30 . . . −0.05]*MGP −0.22 [−0.53 . . . 0.08]*INPP4B   c-indices: trainSet=0.719, independentCohort=0.693   
       replace STC2 by SEC14L2 in member 4:
       −0.18 [−0.31 . . . −0.06]*MGP −0.27 [−0.49 . . . −0.06]*SEC14L2   c-indices: trainSet=0.718, independentCohort=0.681   
       
 
         [0285]    One can see that replacements of single genes experimentally identified for a quantification with kinetic PCR normally affect the predictive performance of the T5 algorithm, assessed by the c-index only insignificantly. 
         [0286]    The following table (Tab. 8) shows potential replacement gene candidates for the genes of T5 algorithm. Each gene candidate is shown in one table cell: The gene name is followed by the bracketed absolute Pearson correlation coefficient of the expression of the original gene in the T5 Algorithm and the replacement candidate, and the HG-U133A probe set ID. 
         [0000]    
       
         
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 8 
               
               
                   
               
               
                 BIRC5 
                 RBBP8 
                 UBE2C 
                 IL6ST 
                 AZGP1 
                 DHCR7 
                 MGP 
                 STC2 
               
               
                   
               
             
             
               
                 UBE2C (0.775), 
                 CELSR2 
                 BIRC5 (0.775), 
                 INPP4B (0.477), 
                 PIP (0.530), 
                 AURKA (0.345), 
                 APOD (0.368), 
                 INPP4B (0.500), 
               
               
                 202954_at 
                 (0.548), 
                 202095_s_at 
                 205376_at 
                 206509_at 
                 204092_s_at 
                 201525_at 
                 205376_at 
               
               
                   
                 204029_at 
               
               
                 TOP2A (0.757), 
                 PGR (0.392), 
                 RACGAP1 (0.756), 
                 STC2 (0.450), 
                 EPHX2 (0.369), 
                 BIRC5 (0.323), 
                 IL6ST (0.327), 
                 IL6ST (0.450), 
               
               
                 201292_at 
                 208305_at 
                 222077_s_at 
                 203438_at 
                 209368_at 
                 202095_s_at 
                 212196_at 
                 212196_at 
               
               
                 RACGAP1 
                 STC2 (0.361), 
                 TOP2A (0.753), 
                 MAPT (0.440), 
                 PLAT (0.366), 
                 UBE2C (0.315), 
                 EGFR (0.308), 
                 SEC14L2 
               
               
                 (0.704), 
                 203438_at 
                 201292_at 
                 206401_s_at 
                 201860_s_at 
                 202954_at 
                 201983_s_at 
                 (0.417), 
               
               
                 222077_s_at 
                   
                   
                   
                   
                   
                   
                 204541_at 
               
               
                 AURKA (0.681), 
                 ABAT (0.317), 
                 AURKA (0.694), 
                 SCUBE2 (0.418), 
                 SEC14L2 
                   
                   
                 MAPT (0.414), 
               
               
                 204092_s_at 
                 209459_s_at 
                 204092_s_at 
                 219197_s_at 
                 (0.351), 
                   
                   
                 206401_s_at 
               
               
                   
                   
                   
                   
                 204541_at 
               
               
                 NEK2 (0.680), 
                 IL6ST (0.311), 
                 NEK2 (0.684), 
                 ABAT (0.389), 
                 SCUBE2 (0.331), 
                   
                   
                 CHPT1 (0.410), 
               
               
                 204026_s_at 
                 212196_at 
                 204026_s_at 
                 209459_s_at 
                 219197_s_at 
                   
                   
                 221675_s_at 
               
               
                 E2F8 (0.640), 
                   
                 E2F8 (0.652), 
                 PGR (0.377), 
                 PGR (0.302), 
                   
                   
                 ABAT (0.409), 
               
               
                 219990_at 
                   
                 219990_at 
                 208305_at 
                 208305_at 
                   
                   
                 209459_s_at 
               
               
                 PCNA (0.544), 
                   
                 PCNA (0.589), 
                 SEC14L2 
                   
                   
                   
                 SCUBE2 (0.406), 
               
               
                 201202_at 
                   
                 201202_at 
                 (0.356), 
                   
                   
                   
                 219197_s_at 
               
               
                   
                   
                   
                 204541_at 
               
               
                 CYBRD1 
                   
                 CYBRD1 (0.486), 
                 ESR1 (0.353), 
                   
                   
                   
                 ESR1 (0.394), 
               
               
                 (0.462), 
                   
                 217889_s_at 
                 205225_at 
                   
                   
                   
                 205225_at 
               
               
                 217889_s_at 
               
               
                 DCN (0.439), 
                   
                 ADRA2A (0.391), 
                 GJA1 (0.335), 
                   
                   
                   
                 RBBP8 (0.361), 
               
               
                 209335_at 
                   
                 209869_at 
                 201667_at 
                   
                   
                   
                 203344_s_at 
               
               
                 ADRA2A 
                   
                 DCN (0.384), 
                 MGP (0.327), 
                   
                   
                   
                 PGR (0.347), 
               
               
                 (0.416), 
                   
                 209335_at 
                 202291_s_at 
                   
                   
                   
                 208305_at 
               
               
                 209869_at 
               
               
                 SQLE (0.415), 
                   
                 SQLE (0.369), 
                 EPHX2 (0.313), 
                   
                   
                   
                 PTPRT (0.343), 
               
               
                 209218_at 
                   
                 209218_at 
                 209368_at 
                   
                   
                   
                 205948_at 
               
               
                 CXCL12 (0.388), 
                   
                 CCND1 (0.347), 
                 RBBP8 (0.311), 
                   
                   
                   
                 HSPA2 (0.317), 
               
               
                 209687_at 
                   
                 208712_at 
                 203344_s_at 
                   
                   
                   
                 211538_s_at 
               
               
                 EPHX2 (0.362), 
                   
                 ASPH (0.344), 
                 PTPRT (0.303), 
                   
                   
                   
                 PTGER3 (0.314), 
               
               
                 209368_at 
                   
                 210896_s_at 
                 205948_at 
                   
                   
                   
                 210832_x_at 
               
               
                 ASPH (0.352), 
                   
                 CXCL12 (0.342), 
                 PLAT (0.301), 
               
               
                 210896_s_at 
                   
                 209687_at 
                 201860_s_at 
               
               
                 PRSS16 (0.352), 
                   
                 PIP (0.328), 
               
               
                 208165_s_at 
                   
                 206509_at 
               
               
                 EGFR (0.346), 
                   
                 PRSS16 (0.326), 
               
               
                 201983_s_at 
                   
                 208165_s_at 
               
               
                 CCND1 (0.331), 
                   
                 EGFR (0.320), 
               
               
                 208712_at 
                   
                 201983_s_at 
               
               
                 TRIM29 (0.325), 
                   
                 DHCR7 (0.315), 
               
               
                 202504_at 
                   
                 201791_s_at 
               
               
                 DHCR7 (0.323), 
                   
                 EPHX2 (0.315), 
               
               
                 201791_s_at 
                   
                 209368_at 
               
               
                 PIP (0.308), 
                   
                 TRIM29 (0.311), 
               
               
                 206509_at 
                   
                 202504_at 
               
               
                 TFAP2B (0.306), 
               
               
                 214451_at 
               
               
                 WNT5A (0.303), 
               
               
                 205990_s_at 
               
               
                 APOD (0.301), 
               
               
                 201525_at 
               
               
                 PTPRT (0.301), 
               
               
                 205948_at 
               
               
                   
               
             
          
         
       
     
         [0287]    The following table (Tab. 9) lists qRT-PCR primer and probe sequences used for the table above. 
         [0000]    
       
         
               
               
               
               
             
           
               
                 TABLE 9 
               
               
                   
               
               
                 gene 
                 probe 
                 forward primer 
                 reverse primer 
               
               
                   
               
             
             
               
                 ABAT 
                 TCGCCCTAAGAGGCTCTTCCTC 
                 GGCAACTTGAGGTCTGACTTTTG 
                 GGTCAGCTCACAAGTGGTGTGA 
               
               
                   
               
               
                 ADRA2A 
                 TTGTCCTTTCCCCCCTCCGTGC 
                 CCCCAAGAGCTGTTAGGTATCAA 
                 TCAATGACATGATCTCAACCAGAA 
               
               
                   
               
               
                 APOD 
                 CATCAGCTCTCAACTCCTGGTTTAACA 
                 ACTCACTAATGGAAAACGGAAAGATC 
                 TCACCTTCGATTTGATTCACAGTT 
               
               
                   
               
               
                 ASPH 
                 TGGGAGGAAGGCAAGGTGCTCATC 
                 TGTGCCAACGAGACCAAGAC 
                 TCGTGCTCAAAGGAGTCATCA 
               
               
                   
               
               
                 AURKA 
                 CCGTCAGCCTGTGCTAGGCAT 
                 AATCTGGAGGCAAGGTTCGA 
                 TCTGGATTTGCCTCCTGTGAA 
               
               
                   
               
               
                 BIRC5 
                 AGCCAGATGACGACCCCATAGAGGAACA 
                 CCCAGTGTTTCTTCTGCTTCAAG 
                 CAACCGGACGAATGCTTTTT 
               
               
                   
               
               
                 CCND1 
                   
                   
                   
               
               
                   
               
               
                 CELSR2 
                 ACTGACTTTCCTTCTGGAGCAGGTGGC 
                 TCCAAGCATGTATTCCAGACTTGT 
                 TGCCCACAGCCTCTTTTTCT 
               
               
                   
               
               
                 CHPT1 
                 CCACGGCCACCGAAGAGGCAC 
                 CGCTCGTGCTCATCTCCTACT 
                 CCCAGTGCACATAAAAGGTATGTC 
               
               
                   
               
               
                 CXCL12 
                 CCACAGCAGGGTTTCAGGTTCC 
                 GCCACTACCCCCTCCTGAA 
                 TCACCTTGCCAACAGTTCTGAT 
               
               
                   
               
               
                 CYBRD1 
                 AGGGCATCGCCATCATCGTC 
                 GTCACCGGCTTCGTCTTCA 
                 CAGGTCCACGGCAGTCTGT 
               
               
                   
               
               
                 DCN 
                 TCTTTTCAGCAACCCGGTCCA 
                 AAGGCTTCTTATTCGGGTGTGA 
                 TGGATGGCTGTATCTCCCAGTA 
               
               
                   
               
               
                 DHCR7 
                 TGAGCGCCCACCCTCTCGA 
                 GGGCTCTGCTTCCCGATT 
                 AGTCATAGGGCAAGCAGAAAATTC 
               
               
                   
               
               
                 E2F8 
                 CAGGATACCTAATCCCTCTCACGCAG 
                 AAATGTCTCCGCAACCTTGTTC 
                 CTGCCCCCAGGGATGAG 
               
               
                   
               
               
                 EGFR 
                   
                   
                   
               
               
                   
               
               
                 EPHX2 
                 TGAAGCGGGAGGACTTTTTGTAAA 
                 CGATGAGAGTGTTTTATCCATGCA 
                 GCTGAGGCTGGGCTCTTCT 
               
               
                   
               
               
                 ESR1 
                 ATGCCCTTTTGCCGATGCA 
                 GCCAAATTGTGTTTGATGGATTAA 
                 GACAAAACCGAGTCACATCAGTAATAG 
               
               
                   
               
               
                 GJA1 
                 TGCACAGCCTTTTGATTTCCCCGAT 
                 CGGGAAGCACCATCTCTAACTC 
                 TTCATGTCCAGCAGCTAGTTTTTT 
               
               
                   
               
               
                 HSPA2 
                 CAAGTCAGCAAACACGCAAAA 
                 CATGCACGAACTAATCAAAAATGC 
                 ACATTATTCGAGGTTTCTCTTTAATGC 
               
               
                   
               
               
                 IL6ST 
                 CAAGCTCCACCTTCCAAAGGACCT 
                 CCCTGAATCCATAAAGGCATACC 
                 CAGCTTCGTTTTTCCCTACTTTTT 
               
               
                   
               
               
                 INPP4B 
                 TCCGAGCGCTGGATTGCATGAG 
                 GCACCAGTTACACAAGGACTTCTTT 
                 TCTCTATGCGGCATCCTTCTC 
               
               
                   
               
               
                 MAPT 
                 AGACTATTTGCACACTGCCGCCT 
                 GTGGCTCAAAGGATAATATCAAACAC 
                 ACCTTGCTCAGGTCAACTGGTT 
               
               
                   
               
               
                 MGP 
                 CCTTCATATCCCCTCAGCAGAGATGG 
                 CCTTCATTAACAGGAGAAATGCAA 
                 ATTGAGCTCGTGGACAGGCTTA 
               
               
                   
               
               
                 NEK2 
                 TCCTGAACAAATGAATCGCATGTCCTACAA 
                 ATTTGTTGGCACACCTTATTACATGT 
                 AAGCAGCCCAATGACCAGATa 
               
               
                   
               
               
                 PCNA 
                 AAATACTAAAATGCGCCGGCAATGA 
                 GGGCGTGAACCTCACCAGTA 
                 CTTCGGCCCTTAGTGTAATGATATC 
               
               
                   
               
               
                 PGR 
                 TTGATAGAAACGCTGTGAGCTCGA 
                 AGCTCATCAAGGCAATTGGTTT 
                 ACAAGATCATGCAAGTTATCAAGAAGTT 
               
               
                   
               
               
                 PIP 
                 TGCATGGTGGTTAAAACTTACCTCA 
                 TGCTTGCAGTTCAAACAGAATTG 
                 CACCTTGTAGAGGGATGCTGCTA 
               
               
                   
               
               
                 PLAT 
                 CAGAAAGTGGCCATGCCACCCTG 
                 TGGGAAGACATGAATGCACACTA 
                 GGAGGTTGGGCTTTAGCTGAA 
               
               
                   
               
               
                 PRSS16 
                 CACTGCCGGTCACCCACACCA 
                 CTGAGGAGCACAGAACCTCAACT 
                 CGAACTCGGTACATGTCTGATACAA 
               
               
                   
               
               
                 PTGER3 
                 TCGGTCTGCTGGTCTCCGCTCC 
                 CTGATTGAAGATCATTTTCAACATCA 
                 GACGGCCATTCAGCTTATGG 
               
               
                   
               
               
                 PTPRT 
                 TTGGCTTCTGGACACCCTCACA 
                 GAGTTGTGGCCTCTACCATTGC 
                 GAGCGGGAACCTTGGGATAG 
               
               
                   
               
               
                 RACGAP1 
                 ACTGAGAATCTCCACCCGGCGCA 
                 TCGCCAACTGGATAAATTGGA 
                 GAATGTGCGGAATCTGTTTGAG 
               
               
                   
               
               
                 RBBP8 
                 ACCGATTCCGCTACATTCCACCCAAC 
                 AGAAATTGGCTTCCTGCTCAAG 
                 AAAACCAACTTCCCAAAAATTCTCT 
               
               
                   
               
               
                 SCUBE2 
                 CTAGAGGGTTCCAGGTCCCATACGTGACATA 
                 TGTGGATTCAGTTCAAGTCCAATG 
                 CCATCTCGAACTATGTCTTCAATGAGT 
               
               
                   
               
               
                 SEC14L2 
                 TGGGAGGCATGCAACGCGTG 
                 AGGTCTTACTAAGCAGTCCCATCTCT 
                 CGACCGGCACCTGAACTC 
               
               
                   
               
               
                 SQLE 
                 TATGCGTCTCCCAAAAGAAGAACACCTCG 
                 GCAAGCTTCCTTCCTCCTTCA 
                 CCTTTAGCAGTTTTCTCCATAGTTTTATATC 
               
               
                   
               
               
                 STC2 
                 TCTCACCTTGACCCTCAGCCAAG 
                 ACATTTGACAAATTTCCCTTAGGATT 
                 CCAGGACGCAGCTTTACCAA 
               
               
                   
               
               
                 TFAP2B 
                 CAACACCACCACTAACAGGCACACGTC 
                 GGCATGGACAAGATGTTCTTGA 
                 CCTCCTTGTCGCCAGTTTTACT 
               
               
                   
               
               
                 TOP2A 
                 CAGATCAGGACCAAGATGGTTCCCACAT 
                 CATTGAAGACGCTTCGTTATGG 
                 CCAGTTGTGATGGATAAAATTAATCAG 
               
               
                   
               
               
                 TRIM29 
                 TGCTGTCTCACTACCGGCCATTCTACG 
                 TGGAAATCTGGCAAGCAGACT 
                 CAATCCCGTTGCCTTTGTTG 
               
               
                   
               
               
                 UBE2C 
                 TGAACACACATGCTGCCGAGCTCTG 
                 CTTCTAGGAGAACCCAACATTGATAGT 
                 GTTTCTTGCAGGTACTTCTTAAAAGCT 
               
               
                   
               
               
                 WNT5A 
                 TATTCACATCCCCTCAGTTGCAGTGAATTG 
                 CTGTGGCTCTTAATTTATTGCATAATG 
                 TTAGTGCTTTTTGCTTTCAAGATCTT 
               
               
                   
               
             
          
         
       
     
         [0288]    A second alternative for unsupervised selection of possible gene replacement candidates is based on Affymetrix data only. This has the advantage that it can be done solely based on already published data (e.g. from www.ncbi.nlm.nih.gov/geo/). The following table (Tab. 10) lists HG-U133a probe set replacement candidates for the probe sets used in algorithms T1-T5. This is based on training data of these algorithms. The column header contains the gene name and the probe set ID in bold. Then, the 10 best-correlated probe sets are listed, where each table cell contains the probe set ID, the correlation coefficient in brackets and the gene name. 
         [0000]    
       
         
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 10 
               
               
                   
               
             
             
               
                 UBE2C 
                 BIRC5 
                 DHCR7 
                 RACGAP1 
                 AURKA 
                 PVALB 
                 NMU 
                 STC2 
               
               
                 202954_at 
                 202095_s_at 
                 201791_s_at 
                 222077_s_at 
                 204092_s_at 
                 205336_at 
                 206023_at 
                 203438_at 
               
               
                   
               
               
                 210052_s_at 
                 202954_at 
                 201790_s_at 
                 218039_at 
                 208079_s_at 
                 208683_at 
                 205347_s_at 
                 203439_s_at 
               
               
                 (0.82) TPX2 
                 (0.82) UBE2C 
                 (0.66) DHCR7 
                 (0.79) NUSAP1 
                 (0.89) STK6 
                 (−0.33) CAPN2 
                 (0.45) TMSL8 
                 (0.88) STC2 
               
               
                 202095_s_at 
                 218039_at 
                 202218_s_at 
                 214710_s_at 
                 202954_at 
                 219682_s_at 
                 203764_at 
                 212496_s_at 
               
               
                 (0.82) BIRC5 
                 (0.81) NUSAP1 
                 (0.48) FADS2 
                 (0.78) CCNB1 
                 (0.80) UBE2C 
                 (0.30) TBX3 
                 (0.45) DLG7 
                 (0.52) JMJD2B 
               
               
                 218009_s_at 
                 218009_s_at 
                 202580_x_at 
                 203764_at 
                 210052_s_at 
                 218704_at 
                 203554_x_at 
                 219440_at 
               
               
                 (0.82) PRC1 
                 (0.79) PRC1 
                 (0.47) FOXM1 
                 (0.77) DLG7 
                 (0.77) TPX2 
                 (0.30) FLJ20315 
                 (0.44) PTTG1 
                 (0.52) RAI2 
               
               
                 203554_x_at 
                 202705_at 
                 208944_at 
                 204026_s_at 
                 202095_s_at 
                   
                 204962_s_at 
                 215867_x_at 
               
               
                 (0.82) PTTG1 
                 (0.78) CCNB2 
                 (−0.46) TGFBR2 
                 (0.77) ZWINT 
                 (0.77) BIRC5 
                   
                 (0.44) CENPA 
                 (0.51) CA12 
               
               
                 208079_s_at 
                 204962_s_at 
                 202954_at 
                 218009_s_at 
                 203554_x_at 
                   
                 204825_at 
                 214164_x_at 
               
               
                 (0.81) STK6 
                 (0.78) CENPA 
                 (0.46) UBE2C 
                 (0.76) PRC1 
                 (0.76) PTTG1 
                   
                 (0.43) MELK 
                 (0.50) CA12 
               
               
                 202705_at 
                 203554_x_at 
                 209541_at 
                 204641_at 
                 218009_s_at 
                   
                 209714_s_at 
                 204541_at 
               
               
                 (0.81) CCNB2 
                 (0.78) PTTG1 
                 (−0.45) IGF1 
                 (0.76) NEK2 
                 (0.75) PRC1 
                   
                 (0.41) CDKN3 
                 (0.50) SEC14L2 
               
               
                 218039_at 
                 208079_s_at 
                 201059_at 
                 204444_at 
                 201292_at 
                   
                 219918_s_at 
                 203963_at 
               
               
                 (0.81) NUSAP1 
                 (0.78) STK6 
                 (0.45) CTTN 
                 (0.75) KIF11 
                 (0.73) TOP2A 
                   
                 (0.41) ASPM 
                 (0.50) CA12 
               
               
                 202870_s_at 
                 210052_s_at 
                 200795_at 
                 202705_at 
                 214710_s_at 
                   
                 207828_s_at 
                 212495_at 
               
               
                 (0.80) CDC20 
                 (0.77) TPX2 
                 (−0.45) 
                 (0.75) CCNB2 
                 (0.73) CCNB1 
                   
                 (0.41) CENPF 
                 (0.50) JMJD2B 
               
               
                 204092_s_at 
                 202580_x_at 
                 SPARCL1 
                 203362_s_at 
                 204962_s_at 
                   
                 202705_at 
                 208614_s_at 
               
               
                   
                   
                 218009_s_at 
               
               
                 (0.80) STK6 
                 (0.77) FOXM1 
                 (0.45) PRC1 
                 (0.75) MAD2L1 
                 (0.73) CENPA 
                   
                 (0.41) CCNB2 
                 (0.49) FLNB 
               
               
                 209408_at 
                 204092_s_at 
                 218542_at 
                 202954_at 
                 218039_at 
                   
                 219787_s_at 
                 213933_at 
               
               
                 (0.80) KIF2C 
                 (0.77) STK6 
                 (0.45) C10orf3 
                 (0.75) UBE2C 
                 (0.73) NUSAP1 
                   
                 (0.40) ECT2 
                 (0.49) PTGER3 
               
               
                   
               
             
          
           
               
                 AZGP1 
                 RBBP8 
                 IL6ST 
                 MGP 
                 PTGER3 
                 CXCL12 
                 ABAT 
                 CDH1 
               
               
                 209309_at 
                 203344_s_at 
                 212196_at 
                 202291_s_at 
                 213933_at 
                 209687_at 
                 209460_at 
                 201131_s_at 
               
               
                   
               
               
                 217014_s_at 
                 36499_at 
                 212195_at 
                 201288_at 
                 210375_at 
                 204955_at 
                 209459_s_at 
                 201130_s_at 
               
               
                 (0.92) AZGP1 
                 (0.49) CELSR2 
                 (0.85) IL6ST 
                 (0.46) ARHGDIB 
                 (0.74) PTGER3 
                 (0.81) SRPX 
                 (0.92) ABAT 
                 (0.57) CDH1 
               
               
                 206509_at 
                 204029_at 
                 204864_s_at 
                 219768_at 
                 210831_s_at 
                 209335_at 
                 206527_at 
                 221597_s_at 
               
               
                 (0.52) PIP 
                 (0.45) CELSR2 
                 (0.75) IL6ST 
                 (0.42) VTCN1 
                 (0.74) PTGER3 
                 (0.81) DCN 
                 (0.63) ABAT 
                 (0.40) HSPC171 
               
               
                 204541_at 
                 208305_at 
                 211000_s_at 
                 202849_x_at 
                 210374_x_at 
                 211896_s_at 
                 213392_at 
                 203350_at 
               
               
                 (0.46) SEC14L2 
                 (0.45) PGR 
                 (0.68) IL6ST 
                 (−0.41) GRK6 
                 (0.73) PTGER3 
                 (0.81) DCN 
                 (0.54) 
                 (0.38) AP1G1 
               
               
                   
                   
                   
                   
                   
                   
                 MGC35048 
               
               
                 200670_at 
                 205380_at 
                 214077_x_at 
                 205382_s_at 
                 210832_x_at 
                 201893_x_at 
                 221666_s_at 
                 209163_at 
               
               
                 (0.45) XBP1 
                 (0.43) PDZK1 
                 (0.61) MEIS4 
                 (0.40) DF 
                 (0.73) PTGER3 
                 (0.81) DCN 
                 (0.49) PYCARD 
                 (0.36) CYB561 
               
               
                 209368_at 
                 203303_at 
                 204863_s_at 
                 200099_s_at 
                 210834_s_at 
                 203666_at 
                 218016_s_at 
                 210239_at 
               
               
                 (0.45) EPHX2 
                 (0.41) TCTE1L 
                 (0.58) IL6ST 
                 (0.39) RPS3A 
                 (0.55) PTGER3 
                 (0.80) CXCL12 
                 (0.48) POLR3E 
                 (0.35) IRX5 
               
               
                 218627_at 
                 205280_at 
                 202089_s_at 
                 221591_s_at 
                 210833_at 
                 211813_x_at 
                 214440_at 
                 200942_s_at 
               
               
                 (−0.43) FLJ11259 
                 (0.38) GLRB 
                 (0.57) SLC39A6 
                 (−0.37) FAM64A 
                 (0.55) PTGER3 
                 (0.80) DCN 
                 (0.46) NAT1 
                 (0.34) HSBP1 
               
               
                 202286_s_at 
                 205279_s_at 
                 210735_s_at 
                 214629_x_at 
                 203438_at 
                 208747_s_at 
                 204981_at 
                 209157_at 
               
               
                 (0.43) TACSTD2 
                 (0.38) GLRB 
                 (0.56) CA12 
                 (0.37) RTN4 
                 (0.49) STC2 
                 (0.79) C1S 
                 (0.45) SLC22A18 
                 (0.34) DNAJA2 
               
               
                 213832_at 
                 203685_at 
                 200648_s_at 
                 200748_s_at 
                 203439_s_at 
                 203131_at 
                 212195_at 
                 210715_s_at 
               
               
                 (0.42)— 
                 (0.38) BCL2 
                 (0.52) GLUL 
                 (0.37) FTH1 
                 (0.46) STC2 
                 (0.78) PDGFRA 
                 (0.45) IL6ST 
                 (0.33) SPINT2 
               
               
                 204288_s_at 
                 203304_at 
                 214552_s_at 
                 209408_at 
                 212195_at 
                 202994_s_at 
                 204497_at 
                 203219_s_at 
               
               
                 (0.41) SORBS2 
                 (−0.38) 
                 (0.52) RABEP1 
                 (−0.37) KIF2C 
                 (0.41) IL6ST 
                 (0.78) FBLN1 
                 (0.45) ADCY9 
                 (0.33) APRT 
               
               
                   
                 BAMBI 
               
               
                 202376_at 
                 205862_at 
                 219197_s_at 
                 218726_at 
                 217764_s_at 
                 208944_at 
                 215867_x_at 
                 218074_at 
               
               
                 (0.41) SERPINA3 
                 (0.36) GREB1 
                 (0.51) SCUBE2 
                 (−0.36) 
                 (0.40) RAB31 
                 (0.78) TGFBR2 
                 (0.45) CA12 
                 (0.33) FAM96B 
               
               
                   
                   
                   
                 DKFZp762E1312 
               
               
                   
               
             
          
         
       
     
         [0289]    After selection of a gene or a probe set one has to define a mathematical mapping between the expression values of the gene to replace and those of the new gene. There are several alternatives which are discussed here based on the example “replace delta-Ct values of BIRC5 by RACGAP1”. In the training data the joint distribution of expressions looks like in  FIG. 3 . 
         [0290]    The Pearson correlation coefficient is 0.73. 
         [0291]    One approach is to create a mapping function from RACGAP1 to BIRC5 by regression. Linear regression is the first choice and yields in this example
       BIRC5=1.22*RACGAP1 −2.85.       
 
         [0293]    Using this equation one can easily replace the BIRC5 variable in e.g. algorithm T5 by the right hand side. In other examples robust regression, polynomial regression or univariate nonlinear pre-transformations may be adequate. 
         [0294]    The regression method assumes measurement noise on BIRC5, but no noise on RACGAP1. Therefore the mapping is not symmetric with respect to exchangeability of the two variables. A symmetric mapping approach would be based on two univariate z-transformations.
       z=(BIRC5−mean(BIRC5))/std(BIRC5) and   z=(RACGAP1−mean(RACGAP1))/std(RACGAP1)   z=(BIRC5 −8.09)/1.29=(RACGAP1 −8.95)/0.77   BIRC5=1.67*RACGAP1 +−6.89       
 
         [0299]    Again, in other examples, other transformations may be adequate: 
         [0300]    normalization by median and/or mad, nonlinear mappings, or others.