ES2343996B1 - METHOD FOR SUBCLASSIFICATION OF TUMORS. - Google Patents

Abstract

Method for tumor subclassification.

The present invention fits within the field of molecular biology and medicine. Specifically, the The present invention relates to a subclassification method of tumors Specifically, it refers to a method of forecasting the Tumor evolution, and therefore, a useful method to take decisions regarding the treatment to be administered to the patient. In addition, the present invention relates to a kit that allows the tumor subclassification by this method.

Description

Method for tumor subclassification.

The present invention fits within the field of molecular biology and medicine. Specifically, the The present invention relates to a subclassification method of tumors Specifically, it refers to a method of forecasting the Tumor evolution, and therefore, a useful method to take decisions regarding the treatment to be administered to the patient. In addition, the present invention relates to a kit that allows the tumor subclassification by this method.

Prior art

Breast cancer is the most malignant tumor common in women and the leading cause of cancer death also in women. It is a heterogeneous disease in terms of clinical manifestations, prognosis and sensitivity or Resistance to different medical treatments. The forecast of breast cancer is particularly relevant because it serves to select the adjuvant treatments that the patient will receive after surgery to reduce the risk of relapse.

In recent years, numerous publications have shown that the new techniques of high performance in genomics can be very useful in achieving these objectives (Marchionni et al . Evid Rep Technol Assess. 2007 Dec; (160): 1-105 ). These techniques can provide information on the risk of suffering from the disease, the presence of a hidden tumor in early detection programs, the early detection of a relapse, the possibility of such a cancer relapse (prognosis) and the possibility of side effects. and response to treatments. Thus, several gene profiles related to the prognosis of breast cancer have been found. Three of these profiles, known as " 70-gene Signature "(van't Veer et al . Nature. 2002 Jan 31; 415 (6871): 530-6), " Recurrence Score " (Paik et al . N Engl J Med. 2004 Dec 30; 351 (27): 2817-26), and " H / I Index " (Ma et al . J Clin Oncol. 2006 Oct 1; 24 (28): 4611-9) have passed an independent validation , what could be called phase II trials.

The " 70-gene Signature " prognostic gene profile, marketed under the name of MammaPrint® by the company Agendia (van't Veer et al . Nature. 2002 Jan 31; 415 (6871): 530-6; patent with Publication number EP1782315) is offered for the diagnosis of patients who have the following criteria: tumor size less than 5 cm, stage I or II breast carcinoma, without lymph node involvement or up to 3 affected lymph nodes, regardless of the expression of the estrogen receptor (ER). The procedure requires the measurement of the expression of 70 genes that determine the gene signature associated with prognosis. The method used for analysis consists of obtaining RNA from fresh frozen (FF, acronym English "Fresh Frozen") and the use of tissue cDNA microarrays. The use of frozen tissue for large-scale molecular studies presents several problems: frozen samples are difficult to collect, complicated to process and expensive to store. Conversely, tissue samples fixed in formalin and paraffin - embedded (FFPE acronym English "Formalin-Fixed Paraffin-Embedded") are stable at room temperature, easy to store and, more importantly, a wide File of clinical samples available along with your clinical information and disease monitoring. Faced with these advantages offered by samples fixed and embedded in formalin, there is a disadvantage that the RNA obtained from these samples is very degraded. While studies using microarrays are very sensitive to RNA degradation, the use of quantitative RT-PCR (qRT-PCR) has proven to be a technique that offers better results against RNA degradation. In addition, expression analysis using microarrays is a complex technique that requires sophisticated equipment that is not available in many laboratories. Therefore, in view of the reasons set forth, the use of qRT-PCR from RNA obtained from FFPE tissue samples used in the present invention offers numerous advantages over microarray expression analysis from RNA obtained from samples. of FF fabric.

In the other two gene profiles with prognostic value mentioned, known as " Recurrence Score " (Paik et al . N Engl J Med. 2004 Dec 30; 351 (27): 2817-26), and " H / I Index " ( Ma et al . J Clin Oncol. 2006 Oct 1; 24 (28): 4611-9), the method used is to obtain RNA from FFPE tumor samples and use the real-time RT-PCR technique , with the advantages that this implies.

The " H / I Index " marketed under the name of Theros H / I ™ by the company BioTheranostics (Ma et al . J Clin Oncol. 2006 Oct 1; 24 (28): 4611-9; patent with publication number WO2007084220) it requires the measurement of the expression of the HOXB13 and IL17BR genes that determine the gene signature associated with prognosis and 4 genes that allow normalization. This method allows to predict the response to hormonal treatment in women with positive breast carcinoma for estrogen receptor expression and without affected nodes.

The " Recurrence Score " profile, marketed under the name of Oncotype DX ™ by Genomic Health, Inc. (Paik et al . N Engl J Med. 2004 Dec 30; 351 (27): 2817-26; patent with number of Publication EP1815014) is offered for the prognosis of patients who have been diagnosed with a positive stage I or II invasive breast carcinoma for estrogen receptor expression and who do not have lymph node involvement. This profile is one of the few for which studies have been carried out that allow to affirm its clinical utility to predict the benefit of a chemotherapeutic treatment (Gianni et al . J Clin Oncol. 2005; 23 (29): 7265-7277; Paik et al . J Clin Oncol. 2006; 24 (23): 3726-3724; Chang et al . Breast Cancer Res Treat. 2008 Mar; 108 (2): 233-40).

Ovarian cancer, although less frequent than breast cancer, is the most lethal tumor of the female genital tract, due not only to its intrinsic aggressiveness, but also to the difficulty of early diagnosis, which makes almost two thirds of Women diagnosed are already in advanced stages of the disease. In the specific case of ovarian cancer, we can also find papers that analyze expression profiles using high performance techniques (Crijns et al , 2006. Int J Gynecol Cancer. 16: 152-65).

In conclusion, cancer is a heterogeneous disease with different tumor subtypes in terms of their prognosis and response to different therapeutic options. During the last years it has been shown that high performance genomic techniques are very useful for predicting the risk of relapse, survival and response to different adjuvant medical treatments. However, the gene profiles described to date for cancer prognosis still require validation tests. On the other hand, the predictive capacity of each type of each profile is quite limited and there is a need for gene profiles that allow predicting the response to new adjuvant therapies (Marchionni et al . Evid Rep Technol Assess. 2007 Dec; (160): 1-105).

Explanation of the invention.

The present invention relates to a method of tumor subclassification. Specifically, it refers to a method of prognosis of the evolution of tumors, and therefore, a method useful for making decisions regarding the treatment to be administered to the patient. In addition, the present invention relates to a kit that It allows the subclassification of tumors by this method.

Therefore, a first aspect of the invention is refers to a method for the subclassification of tumors that understands:

to.

obtaining a biological sample isolate comprising tumor cells of the mammal;

b.

product quantity detection of the expression of between two and eight genes selected from among the following: DTL, ECT2, MTDH, PRC1, RFC4, SCUBE2, STK32B or ZNF533, in the sample obtained in (a) and

C.

comparison of the amount detected in step (b) with a reference amount.

Another aspect of the present invention is refers to a method of prognosis of tumor evolution that It comprises, in addition to steps (a) - (c) described above, a step (d) where an amount detected in step (b) of the genes DTL, ECT2, MTDH, PRC1 or RFC4 greater than the reference quantity with the one that is compared in step (c) or an amount detected in step (b) of the SCUBE2, STK32B or ZNF533 genes less than the amount of reference to the one compared in step (c) is indicative of a lower survival free from distant relapse or lower global survival

The term "prognosis", as used in the present description, refers, but is not limited, to the probability of death due to cancer or progression, including relapse, metastatic dissemination capacity or response to a certain treatment of a disease neoplastic

The term "prediction," as used in the present description, refers, but is not limited, to the likelihood of a patient responding favorably or unfavorably to a certain treatment, and to the extent of these responses, or that the patient survives, after the surgical removal of a primary tumor and / or chemotherapy by a period of time without cancer relapse.

The term "relapse-free survival to distance "(SLRD), as used herein description, refers to the time elapsed since the date of surgery until distant relapse or until the last visit. He term "overall survival" (SG), as used in This description refers to the time since the date of surgery until the last visit or until death of the patient

The term "isolated biological sample that comprises tumor cells ", as used in the description refers, but is not limited, to tissues and / or fluids biological of a subject, obtained by any method known by an expert in the field that serves this purpose. The biological sample can be a tissue, for example, but without be limited, a tumor biopsy or a fine needle aspirate, or it may be a biological fluid, for example, but not limited to, a fluid sample, such as blood, plasma, serum, lymph, fluid ascites, urine or breast exudate. The sample can be taken from a human mammal, but also non-human mammals, as per example, but not limited to rodents, ruminants, felines or canines The biological sample can be fresh, frozen, fixed, embedded in paraffin. Preferably, the sample is fixed and embedded in paraffin.

The term "product of expression", such and as used in the description, it refers to its products of transcription or expression (RNA or protein). Or in any way resulting from the processing of said transcription products or expression.

The term "gene expression profile" or "gene expression profile", as used in the This description refers to any method that allows quantification of messenger RNA (mRNA) and / or protein in a biological sample

The term "reference quantity" or "cut-off point", as used herein description, refers to any value derived from the quantification of the product of gene expression in a biological sample, which allows to define two populations with different risk of distant relapse.

The detection of the amount of product of the expression of the genes in the sample obtained, as used in the description, refers to the measure of the quantity or concentration, preferably semi-quantitatively or quantitatively. The measure can be carried out directly or indirectly. Direct measurement refers to the measure of the quantity or concentration of the product of the gene expression based on a signal that is obtained directly from the product of the gene expression and that is directly correlated with the number of molecules of the product of the gene. gene expression present in the sample. Said signal - which we can also refer to as an intensity signal - can be obtained, for example, by measuring an intensity value of a chemical or physical property of the expression product. The indirect measurement includes the measurement obtained from a secondary component (for example, a component other than the product of gene expression) or a biological measurement system (for example the measurement of cellular responses, ligands, "labels" or enzyme reaction products -
ethics).

In accordance with the present invention, the detection of the amount of product of gene expression can be carried out by any method of determining the amount of the product of gene expression known to the person skilled in the art. In a preferred embodiment, the detection of the gene expression product is performed by determining the level of mRNA derived from its transcription where the analysis of the level of mRNA can be performed, by way of illustration and without limiting the scope of the invention, by amplification by polymerase chain reaction (PCR), back transcription in combination with the ligase chain reaction (RT-LCR), back transcription in combination with the polymerase chain reaction (RT-PCR), back transcription in combination with the quantitative polymerase chain reaction (qRT-PCR), or any other nucleic acid amplification method; DNA microarrays made with oligonucleotides deposited by any mechanism; DNA microarrays made with oligonucleotides synthesized in situ by photolithography or by any other mechanism; in situ hybridization using specific probes labeled with any method of marking; by electrophoresis gels; by membrane transfer and hybridization with a specific probe; by NMR or any other diagnostic imaging technique using paramagnetic nanoparticles or any other type of detectable nanoparticles functionalized with antibodies or by any other means.

The mRNA can be extracted, for example, but not limited to samples of fresh tissue, FF tissue or FFPE tissue samples. The methods of obtaining total RNA or mRNAs are well known in the state of the art. The use of FFPE tissue samples presents significant advantages over FF tissue samples: are stable at room temperature, easy to store and there is a large archive of clinical samples available along with your clinical information and monitoring of the disease. Therefore, in a preferred embodiment the mRNA is extracted from samples of FFPE tissue.

RNA obtained from FFPE tissue samples is usually Be very degraded. While studies through Microarrays are very sensitive to RNA degradation, using the Quantitative RT-PCR (qRT-PCR) has proved to be a technique that offers better results in the face of RNA degradation. In addition, expression analysis by Microarray is a complex technique that requires equipment Sophisticated that are not available in many laboratories. By therefore, in a preferred embodiment the mRNA detection is performed using the qRT-PCR technique.

The bias that can introduce RNA loss amplifiable due to degradation must also be corrected through standardization strategies that can compensate for this RNA degradation effect. Therefore, an aspect of the invention consists of a method of standardization, which from now on later we will refer to as NorMean, which serves to correct the errors due to the different enzymatic efficiencies of the retrotranscription and amplification reactions, as well as caused by variations due to different RNA quality due to degradation due to formalin fixation.

Once selected the appropriate gene or group of genes that function as reference gene normalization factor (NF, English acronym "Normalization Factor") is calculated using the geometric mean of the selected genes.

The NorMean standardization method allows select a gene or group of genes that are used as genes from reference based on the value of parameter a_ {i}:

a_ {i} = \ SigmaCV_ {ij} / \ Sigmar_ {ij}

Where CV_ {ij} is the coefficient of variation of the reference gene i in material j, and r_ {ij} is Pearson's correlation coefficient between gene expression i and the mean expression of all genes in each sample in the material j. The value a_ {i} allows to sort the genes of reference: those reference genes with the lowest value a_ {i} are those with the most stable expression (low CV) and with greater correlation with the mean gene expression per sample (high r). He NF is determined by calculating the geometric mean of the values of expression of the selected reference genes. The optimal number of reference genes is determined by comparing the percentage of genes with significant correlation between FF tissue samples and FFPE tissue for each normalization factor. The data is calculated using the normalized expression values for each gene in each type of sample given by the value 2 -? Ct, where: \ DeltaCt = Ct_ {gen} - NF

To determine the agreement between the data of FF and FFPE tissue samples the coefficient of Pearson's correlation between expression values normalized

Using the NorMean method, the variations in mRNA expression due to different Enzymatic efficiencies of the detection method as well as the due to the degradation of mRNA in FFPE tissues.

The expression of 83 genes, included in three gene profiles related to the prognosis of breast cancer, in samples of patients with infiltrating ductal carcinoma of the breast, was normalized using NorMean. Next, by calculating a Pearson correlation between the normalized data of a series of FF tissue samples with their paired FFPE tissue samples, those genes whose correlation was comparable between both types of tissue (53 genes) were chosen. Subsequently, a univariate analysis allowed to know which of them is associated with distance-free relapse survival (SLRD) based on their p value (17 genes). With 17 genes, a gene profile was constructed that identified a group of patients at high risk of relapse. Gene profiles with 10, 9, 8, 7 or even up to 2 genes also showed a good separation between groups of high and low risk of distant relapse. Finally, an analysis of the main components and a study of the correlation of genes with each other has allowed us to distinguish a profile of 8 genes with prognostic capacity, called " 8-gene Score ". Therefore, in a preferred embodiment, in step (b) of the method of the present invention, the amount of the expression product of between 2 and 8 genes selected from the following is detected: DTL, ECT2, MTDH, PRC1, RFC4, SCUBE2, STK32B or ZNF533. In a very preferred embodiment, in step (b) of the method of the present invention the amount of the product of the expression of the DTL, ECT2, MTDH, PRC1, RFC4, SCUBE2, STK32B and ZNF533 genes is detected. The detection of the amount of product in step (b) of a larger number of genes, which include at least 2 of these 8 genes could reach similar results.

The 8 gene profile of the present invention is calculated in each sample using standardized gene expression measures based on the following equation: " 8-gene Score " = (from 0.0968 to 0.2904) * DTL + (from 0.1088 to 0.3264) * ECT2 + (from 0.0227 to 0.0681) * MTDH + (from 0.0664 to 0.1994) * PRC1 + (from 0.0278 to 0.0834) * RFC4 - (from 0.0956 to 0.2870) * SCUBE2 - (from 0.0221 to 0.0665) * STK32B - (from 0.0591 to 0.1773) * ZNF533. With the intention of assigning each patient to a risk group, a cut-off point is defined, so that patients with a lower profile score than the cut-off point are assigned to the low-risk group for distant relapse and patients with A higher score than said cut-off point constitute the group with a high risk of distant relapse.

An amount detected in step (b) of the DTL, ECT2, MTDH, PRC1 or RFC4 genes greater than the amount of reference to the one compared in step (c) or an amount detected in step (b) of the minor SCUBE2, STK32B or ZNF533 genes that the reference quantity with that compared in step (c) It is indicative of a lower relapse-free survival at distance or lower overall survival.

Due to the multigenic nature of predictor, the replacement of these genes with genes that coexpress with them with a Pearson correlation coefficient r \ geq 0.4 It does not alter the discriminative capacity of the predictor. Examples of These genes are, but not limited to: AYTL2, BIRC5, CCNB1, CCNE2, GMPS, MCM6, MELK, MYBL2, ORC6L, PGR or TGFB3.

Therefore, the present invention provides a method for the subclassification of tumors and / or for the prognosis of the evolution of the tumor in a simple and effective way.

The terms "tumor" or "tumor", such and as used in the present description, it refers to cells transformed that show uncontrolled growth. Depending of its possible evolution can be a benign tumor, which it remains in its starting place and does not produce metastasis; or tumor malignant or cancer, invasive or metastasis. Therefore the term "cancer" or "cancerous" as used in the present description, refers to an alteration of the cells Tumors that have the ability to invade tissues or produce metastasis in distant places of the primary tumor. Examples of Cancer include, but is not limited to, breast cancer, cancers gynecological, colon cancer, prostate cancer, skin cancer, Hepatocellular cancer, lung cancer, esophageal cancer, cancer gastric, pancreatic cancer, bladder cancer, liver cancer, urinary tract cancer, thyroid cancer, kidney cancer, melanoma, brain cancer, sarcoma, lymphoma or leukemia. Within gynecological cancers include ovarian, uterine tumors, of cervix, vagina and vulva. There is a group of tumors in the that the participation of hormonal regulation is important, such such as breast, gynecological or prostate tumors. So, preferably, the method of the present invention is referred to, but it is not limited to a method for the subclassification of tumors of breast, gynecological or prostate.

In a more preferred embodiment, the method of The present invention relates to a method for subclassification of breast tumors. The breast tumor is the accelerated, disorderly and uncontrolled cell proliferation belonging to different tissues of a mammary gland. The Word carcinoma is applied to malignant tumors that originate in cell lines of epithelial or glandular origin. most of breast carcinoma has its origin in accelerated proliferation and uncontrolled of the cells that cover the interior of the ducts that during breastfeeding carry milk from the acini glandular, where it occurs, to the galactophores ducts, located behind the areola and the nipple, where it accumulates on hold to go outside; this breast cancer is known as carcinoma ductal In the remaining cases the cancer has its origin in the glandular acini itself and is called lobular carcinoma.

Many breast carcinomas are confined in the lumen of the ducts or acini, without invading neighboring tissues; They are called carcinomas in situ . When they proliferate too much they can break the so-called basement membrane and spread by infiltrating the tissues surrounding ducts and acini and then receive names such as infiltrating ductal carcinoma or infiltrating lobular carcinoma.

The TNM staging system ( Tumor size, Node, Metastasis ) is the most common method to subclassify breast carcinoma. It allows breast carcinomas to be included in 5 possible stages (0, I, II, III and IV) depending on the size of the tumor (T), the spread to the lymph nodes (N), and metastasis or spread to other parts of the body (M).

The expressions "without affecting the lymph nodes "," without affected lymph nodes ", such and as used in the present description, it refers to a cancer that has not spread to the lymph nodes. Conversely, the "term with lymph node involvement" or "with Affected lymph nodes ", as used in the This description refers to a cancer that has spread to less to a lymph node.

More preferably the method of the present invention refers, but is not limited to a method for subclassification of infiltrating breast carcinomas. Even more preferably, for subclassification of breast carcinomas Stage I or II infiltrants.

Another factor that allows to subclassify cancer breast is the expression of estrogen receptors or progesterone. The expressions "positive for receivers hormonal "as used herein, it refers to a tumor that has expression for the recipient of estrogen and / or progesterone. The expression "positive for receptor of estrogens "as used in the present description, refers to a tumor that has expression for the recipient of estrogen The expression "positive for receptor of progesterone "as used in the present description, refers to a tumor that has expression for the recipient of progesterone. Preferably, the method of the present invention will be refers, but is not limited, to a method for the subclassification of positive breast tumors for receptor expression of estrogen

The main cancer treatment It is usually surgery. In the case of breast cancer, the surgery usually consists of either a mastectomy or a conservative surgery The expression "mastectomy", as it is used in the present description, refers to the removal of the breast or as much breast tissue as possible. In most of the cases, the surgeon also removes the lymph nodes from the armpit. The term "conservative surgery expression", as it is used in the present description, refers to the removal of the tumor but not of the breast; It is also known as lumpectomy, segmental mastectomy or partial mastectomy.

After surgery, many patients receive adjuvant therapy The term "adjuvant therapy," as used in the present description, refers to the treatment that It is given after the main treatment in order to prevent Relapse of cancer in the breast or elsewhere. In the treatment of cancer, adjuvant therapy may consist, for example, but without being limited in radiotherapy, chemotherapy, hormonal therapy or biological therapy

Some patients before surgery receive "neoadjuvant" therapy. The term "neoadjuvant therapy", as used in the present description, it refers to the adjuvant therapy given before the main treatment in order to reduce the size of the tumor, to enable or Facilitate surgery In the treatment of cancer said therapy, may include, for example, but not limited to radiation therapy, Chemotherapy, hormone therapy or biological therapy.

Radiation therapy involves the use of ionizing radiation to destroy cancer cells. After of conservative breast surgery, most patients They receive radiotherapy. Some patients receive radiotherapy after of a mastectomy. Some patients receive radiotherapy before Surgery.

Positive tumors for hormonal receptors they are usually treated with hormonal therapy with the aim of reducing or stop the effects of estrogen on tumor cells. The hormonal therapy, may consist, for example, but not limited to, in the administration of selective receptor modulators of estrogen, aromatase inhibitors, hormone agonists releasing luteinizing hormones or other hormonal agents. Some selective estrogen receptor modulators are, by example, but not limited to tamoxifen, raloxifene or toremifene. Some aromatase inhibitors are, for example, but without Limit letrozole, anastrozole or exemestane. Other substances anti-estrogenic: fulvestrant. Other agents Hormonal are, for example, but not limited to, androgens.

Chemotherapy involves the use of compounds or combinations of compounds with the aim of destroying cancer cells Compounds administered in chemotherapy for breast cancer they can be for example, but not limited, alkylating agents, antimetabolites, mitosis inhibitors or antitumor antibiotics Some alkylating agents are, by example, but not limited to, cisplatin, cyclophosphamide, dacarbazine, ifosfamide, mechlorethamine or melphalan. Some antimetabolites, are for example, but not limited to, fluorouracil, methotrexate, gemcitabine, cytarabine or fludarabine. Some Mitosis inhibitors are, for example, but not limited paclitaxel, docetaxel, etoposide, vinblastine, vincristine or Vinorelbine. Some antitumor antibiotics are, for example, but without limiting bleomycin or anthracyclines. Some anthracyclines they are, for example, daunorubicin, doxorubicin, epirubicin or idarubicin

In a preferred embodiment, the sample Biological is isolated from a mammal that has undergone therapy hormonal and either a mastectomy or a conservative surgery followed by radiotherapy

The method for the subclassification of tumors of the present invention is useful for predicting the cancer response to a certain treatment. In a preferred embodiment, the method for tumor subclassification of the present invention It is useful for making decisions regarding adjuvant treatment to a main treatment administered to the patient. In a alternative embodiment, the method for the subclassification of tumors of the present invention is useful for making decisions with regarding neoadjuvant treatment to a main treatment administered to the patient

In a preferred embodiment, the method for tumor subclassification of the present invention is useful for predict cancer response to anthracycline treatment of a patient with stage I or II infiltrating ductal carcinoma who have received hormonal therapy and mastectomy or surgery Conservative followed by radiation therapy. In one embodiment alternative, the method for the subclassification of tumors of the The present invention is useful for predicting the response of cancer to treatment with cyclophosphamide methotrexate fluorouracil (CMF) of a patient with stage I infiltrating ductal carcinoma or II who has received hormonal therapy and a mastectomy or a Conservative surgery followed by radiation therapy.

In another preferred embodiment, the method of The present invention refers to, but is not limited to, a method for Subclassification of ovarian tumors. The ovarian tumor is the accelerated, disorderly and uncontrolled cell proliferation belonging to different tissues of the ovary. Depending on cell origin, ovarian tumors can be, by example, but not limited to, epithelial tumors, tumors of germ cells, stromal sex cord tumors, lipid cell tumors or gonadoblastomas. The type of tumor more Frequent is the one that originates in the epithelial cells or tumor epithelial ovary Depending on the type of epithelium in which it originates epithelial ovarian tumors can be for example but not limited, serous, mucinous, endometrioid tumors of clear or Brenner cells. Preferably, the method of The present invention refers to, but is not limited to, a method for subclassification of epithelial ovarian tumors. Plus preferably, the method of the present invention is referred to, but is not limited to a method for tumor subclassification serous ovarian epithelials.

Another aspect of the invention relates to a kit adapted to carry out the subclassification method of tumors, which comprises the means to determine the products of expression of between two and eight genes selected from among following: DTL, ECT2, MTDH, PRC1, RFC4, SCUBE2, STK32B or ZNF533, in an isolated biological sample that comprises the patient's cells. Said kit may comprise primers, probes, antibodies monoclonal, and all those reagents necessary to analyze the variation in the level of gene expression by means of any of the methods described earlier in this document and / or known in the state of the art. The kit can also include, without any limitation, the use of buffers, enzymes, polymerase enzymes, cofactors to obtain an optimal activity of these, agents to prevent pollution, etc. On the other hand the kit can include all the supports and containers necessary for its implementation and optimization. Preferably, the kit It also includes the instructions for carrying out the first method of the invention

Unless defined otherwise, the terms scientists and technicians used throughout this description and the claims have the same meaning as understood by an expert in the field belonging to the state of the art of which the present invention is a part.

Throughout the description and the claims the word "comprises" and its variants not they intend to exclude other technical characteristics, additives, components or steps. For those skilled in the art, other objects, advantages and features of the invention will be partly detached of the description and in part of the practice of the invention. The following figures and examples are provided by way of illustration, and are not intended to be limiting of the present invention.

Description of the figures

Figure 1. Shows the percentage of genes significantly correlated between FF tissue samples and FFPE tissue using different normalization methods. It allows compare the effectiveness of the different standardization methods to the time to correct errors due to different efficiencies enzymatic retrotranscription and amplification reactions, as well as those caused by variations due to the different RNA quality due to degradation due to fixation in formalin

Figure 2. It shows the Kaplan-Meier curves that show SLRD for the low-risk and high-risk relapse groups in the groups defined by the " 8-gene Score ".

Figure 3. It shows the sub-analysis of the " 8-gene Score " according to the lymph node involvement. (A) Positive nodes. (B) Negative nodes.

Figure 4. Shows the Kaplan-Meier curves that show the SLRD in the low and high risk patient groups defined by the " 8-gene Score " in the databases used for validation.

Figure 5. It shows the Mann-Whitney test that shows that the " 8-gene Score " is able to differentiate serous ovarian tumors with low malignant potential (LMP) from serous ovarian tumors of the invasive type.

Examples

The following specific examples to be provided in this patent document serve to illustrate the nature of the present invention. These examples are included For illustrative purposes only and should not be construed as limitations to the invention claimed herein. Therefore, the Examples described below illustrate the invention without limiting the field of application of it.

Example 1 Development of a standardization method to compensate for RNA degradation effect

The use of tissue samples fixed in formalin and paraffin - embedded (FFPE acronym English "Formalin-Fixed Paraffin-Embedded") presents significant advantages over tissue samples frozen (FF, acronym of the English "Fresh Frozen") : they are stable at room temperature, easy to store and there is a large archive of clinical samples available along with their clinical information and disease monitoring. Faced with these advantages offered by FFPE tissue samples, there is a disadvantage that the RNA obtained from these samples is very degraded. The bias that can introduce this loss of amplifiable RNA can be corrected by normalization strategies that can compensate for this effect of RNA degradation.

Materials and methods 1. Clinicopathological characteristics of the patients included in the study

For the study of data comparison of FF tissue and FFPE tissue samples were selected from 30 patients diagnosed with infiltrating breast carcinoma for which there was both a sample of FF tissue and FFPE tissue, both obtained from the same surgery.

2. Selection of genes

For the study of comparison of gene expression data from 30 samples of FF tissue and FFPE tissue, a series of previously described genes were selected from the following described gene profiles: " 70-gene Signature "(van't Veer et al . Nature. 2002 Jan 31; 415 (6871): 530-6), " Recurrence Score " (Paik et al . N Engl J Med. 2004 Dec 30; 351 (27): 2817-26), " H / I Index "(Ma et al . Cancer Cell. 2004 Jun; 5 (6): 607-16), and" 76-Gene Prognostic Signature "(Wang et al . Lancet 2005; 365: 671-9), all described with value Prognosis in breast cancer, as well as several reference genes (18S, ACTB, B2M, GAPDH, GUSB, HMBS, HPRT1, IPO8, PGK1, POLR2A, PPIA, RPLP0, SDHA, TBP, TFRC, UBC, YWHAZ).

Two microfluidic card configurations (" Taqman Low Density Arrays ", TLDAs) were used in this comparison analysis of FF and FFPE tissue data. One of them consisted of a configuration of 95 genes in which 78 genes from the studies of Van't Veer and collaborators, Paik and collaborators, and Ma and collaborators, as well as 17 reference genes were amplified. In a second configuration of 63 genes, 50 genes from the study by Wang et al. Were measured along with 13 reference genes. In both cases each card included cDNA from two different samples, the one corresponding to the FF tissue sample and the one corresponding to the FFPE tissue sample, both of the same biopsy.

3. RNA isolation and cDNA synthesis

In order to select the samples of FFPE tissue suitable for the study, sections were obtained stained with hematoxylin / eosin that were analyzed by a pathologist with experience in breast. Samples were chosen that present at least 70% of tumor cells. 5 sections µm of each of the FFPE tissue samples were dewaxed by extraction with xylene, and washing with ethanol in decreasing concentrations (100%, 90% and 70%). RNA was extracted using the Master Puré Purification (Epicenter) kit, according to the manufacturer specifications The RNA of the FF samples is extracted with TRIzol reagent (Invitrogen) according to manufacturer specifications

The isolated RNA was quantified by spectrophotometry, measuring absorbance at 260 and 280 nm. So same, the quality of the different RNAs was evaluated through native electrophoresis in 1% agarose gels, and electrophoresis capillary using the Agilent 2100 Bioanalyzer (Agilent) Technologies). Total RNA isolated and analyzed was re-transcribed using the High Capacity cDNA Reverse Transcription (Applied) kit Biosystems) using 1 µg of RNA in 100 µL reaction in a GeneAmp PCR System 2700 thermal cycler (Applied Biosystems), using random hexamers as primers.

4. Quantitative RT-PCR

The qRT-PCR reactions are carried out in an ABI PRISM 7900 HT Sequence thermal cycler Detection System (Applied Biosystems). As support they were used microfluidic cards (Taqman Low Density Arrays, TLDAs), also designed by Applied Biosystems. Each of the ports of the microfluidic card contained a final volume of 100 µL, 50 µL of Taqman Universal PCR Master Mix and 50 µL of cDNA equivalent to 100 ng of total RNA. The expression of each gene is measured in duplicate in the configuration of 95 genes and in triplicate in that of 63 genes.

5. Calculations for gene expression profiles

The average values of crude threshold cycle (Ct, English "Threshold Cycle"), defined as the point from which the fluorescence is clearly distinguishable from the baseline fluorescence, by SDS 2.2 (Applied Biosystems) were obtained software. The maximum Ct value was set at 40. These Ct values were noted in Excel 2003 for later calculations. Gene expression data was also analyzed using the GenEx software from MultiD Analysis (www.multid.se) and Prism4 from Graphpad (www.graphpad.com).

Gene expression values were calculated. using the ΔCt method described previously (Livak KJ, Schmittgen TD. Methods 2001 Dec; 25 (4): 402-8). Expression values relative were calculated as differences between Ct values of each gene and a normalization factor.

6. Normalization Factors

In this study, comparing data from FF tissue and FFPE tissue, several normalization methods were analyzed and compared: geNorm (Vandesompele et al . Genome Biol 2002; 3: RESEARCH0034), Normfinder (Andersen et al . Cancer Res 2004; 64 : 5245-50), the average expression of all genes, as well as a method developed by the inventors called NorMean. Once selected the appropriate gene or group of genes that function as reference gene normalization factor (NF, its acronym "Normalization Factor") using the geometric mean of the genes selected in each method was calculated.

The reference gene selection method developed by the inventors, NorMean, allows to select genes reference based on the value of parameter a_ {i}:

a_ {i} = \ SigmaCV_ {ij} / \ Sigmar_ {ij}

Where CV_ {ij} is the coefficient of variation of the reference gene i in material j, and r_ {ij} is Pearson's correlation coefficient between gene expression i and the mean expression of all genes in each sample in the material j. The value a_ {i} allows to sort the genes of reference: those reference genes with the lowest value a_ {i} are those with the most stable expression (low CV) and with greater correlation with the mean gene expression per sample (high r). He normalization factor is determined by calculating the geometric mean of the expression values of the reference genes selected.

7. Presentation of the Data

The data was calculated using the values of normalized expression for each gene in each type of sample given by the value 2 ^ - ΔCt}, where: ΔCt = Ctgen-NF.

8. Statistical Analysis

To determine the agreement between the data of FF and FFPE tissue samples the coefficient of Pearson's correlation between normalized expression values with each of the normalization factors for each gene.

Results

Each normalization method generates an NF own, using different reference genes (table one). The percentage of genes with a Pearson correlation value significant between the two types of material was used to assess the ability to correct the effect of conservation on FFPE samples of each of the normalization factors (Figure 1). In the aforementioned figure, it is observed that the method of developed standardization, NorMean, is the most stable among different methods analyzed, since in the two configurations of cards, more than 80% of the genes showed a good correlation between FF fabric and FFPE fabric.

TABLE 1 Reference genes selected by each method in each material

one

Example 2 Development and validation of the gene profile with prognostic capacity " 8-gene-Score " Materials and methods 1. Patients and Clinical Data

For the development of the profile they were collected from retrospective clinical features and Pathological studies of 206 patients diagnosed between 1996 and 2002 of an infiltrating ductal breast carcinoma in the Hospital La Paz University (HULP). The following were established inclusion criteria:

\ sqbullet

Pathological diagnosis of infiltrating ductal breast carcinoma.

\ sqbullet

Stage I or II pathological according to the TNM classification of 2002.

\ sqbullet

Sample preserved in paraffin available.

\ sqbullet

Hormonal receptors positive.

\ sqbullet

Optimal treatment, defined how:

\ Box

Mastectomy or conservative surgery, with infiltration-free surgical margins.

\ Box

Breast radiation therapy after conservative surgery

\ Box

Hormone therapy

\ sqbullet

Clinical follow-up greater than 60 months (except in cases of early relapse).

\ sqbullet

Absence of breast cancer contralateral

All tumors included in the study corresponded to stages I and II, defined according to the TNM pathological staging system (acronym " Tumor size, Node, Metastasis ") for malignant tumors of 2002 used by the UICC (" Union Internationale Contre" le Cancer ") and the AJCC (" American Joint Committee on Cancer "). Tumors with negative or unknown hormonal receptors were excluded. Likewise, cases that had not received hormone therapy as part of their treatment were excluded, since these cases would have received suboptimal treatment. Finally, those cases for which there was no clinical history available and those samples whose extraction product did not have sufficient quality were excluded. Finally, the study included 153 cases.

The clinical characteristics and pathological pathways that were collected retrospectively were:

\ sqbullet

Age at diagnosis (years).

\ sqbullet

Date of diagnosis.

\ sqbullet

Histological type: only admitted cases of infiltrating ductal carcinoma.

\ sqbullet

Primary tumor size (mm)

\ sqbullet

Node Status axillary: affects (positive) or not (negative).

\ sqbullet

Number of nodes affections

\ sqbullet

Degree of differentiation: 1, 2 or 3.

\ sqbullet

Hormonal receptors: for your entered in the analysis, the case had to present positivity at least for the estrogen receptor or progesterone receptor.

\ sqbullet

Adjuvant treatment: Chemotherapy (indicating the scheme administered in each case), hormone therapy and / or radiotherapy.

\ sqbullet

Date of relapse.

\ sqbullet

Initial location of the relapse: crazy-regional or remote (with specification of affected organs).

\ sqbullet

Relapse-free survival Remote (SLRD): time elapsed since the date of surgery Until distant relapse.

\ sqbullet

Global Survival (SG): time elapsed from the date of surgery until the last visit or until the death of the patient.

This study was approved by the Ethics Committee of La Paz University Hospital (HULP).

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2. RNA isolation and cDNA synthesis

It was carried out by the method described in Example 1

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3. Quantitative RT-PCR

It was carried out by the method described in Example 1

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4. Gene Selection

Genes were selected that were included in a microfluidic card (TLDA) with a 96 configuration and containing the available assays for the " 70-gene Signature " genes (van't Veer et al . Nature. 2002 Jan 31; 415 (6871 ): 530-6.), Of the " Recurrence Score " (Paik et al . N Engl J Med. 2004 Dec 30; 351 (27): 2817-26), as well as for the genes of the " H / I Index " ( Ma et al . J Clin Oncol. 2006 Oct 1; 24 (28): 4611-9). In addition, several reference genes were included (POLR2A, IPO8, SDHA, HMBS, UBC, RPLP0, ACTB, PPIA, GAPDH, B2M, GUSB, HPRT1,
TFRC).

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5. Calculations for gene expression profiles

The average values of crude threshold cycle (Ct, English "Threshold Cycle"), defined as the point from which the fluorescence is clearly distinguishable from the baseline fluorescence by SDS 2.2 (Applied Biosystems) were obtained software. The maximum Ct value was set at 40. These Ct values were noted in Excel 2003 for later calculations. Ct values were normalized using the four selected reference genes (IPO8, HMBS, POLR2A and SDHA).

Gene expression values were calculated. using the ΔCt method described previously (Livak and Schmittgen Methods 2001 Dec; 25 (4): 402-8). Relative expression values were calculated as differences. between the Ct values of each gene and a normalization factor (NF), calculated with the geometric mean of the expression of the four reference genes selected by NorMean in example 1 (IPO8, POLR2A, UBC and SDHA):

ΔCt = NF- Ctgen

Normalized expression was adjusted by taking as 0 the lowest expression value, so that an increase of one unity in the expression will reflect approximately a duplication in the amount of RNA

       \ newpage
    

6. Methodology used in the search for a profile of reduced gene expression

First of all those genes were discarded with low correlation between FF and FFPE tissue samples, in based on the results obtained in example 1. A level was calculated of statistical significance for each gene based on the model of Cox proportional hazards or Cox model (Cox. J Roy Stat Soc. 1972; 34: 187-220), in order to identify those genes whose expression was related to survival free from remote relapse. The genes related to SLRD were subsequently filtered based on their p-value and the correlation between themselves, selecting those genes with lowest p value in each correlation group. These genes selected were used to develop a predictor of risk of relapse based on gene expression using the method supervised main components of Tibshirani and Bair (Bair and Tibshirani. PLoS Biol. 2004 Apr; 2 (4): E108).

The LOOCV (" Leave-One-Out-Cross-Validation ") was used to evaluate the predictive value of the method. To test the statistical significance of the cut-off point, the p-value of the statistical " log-rank " test was evaluated among the risk groups using 2000 random permutations. The analyzes were carried out using BRB-ArrayTools v3.6.1 (developed by Richard Simón and Amy Peng).

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7. Statistical Analysis

To evaluate the prognostic value of the " 8-gene Score " in our population, a Kaplan-Meier analysis was carried out and the groups were compared using the " log-rank " test. A univariate and multivariate analysis of Cox was also applied, in a model that included the tumor grade (1 vs 2 and 1 vs 3), size (≤2 cm vs.> 2 cm) and nodal status (no lymph node involvement vs. one to three positive nodes). The assessment criterion was SLRD, as in other studies of gene profiles in breast cancer.

To measure the strength of association between the " 8-gene Score " and the " 70-gene Signature " and the " Recurrence Score ", double-entry contingency table analysis was carried out and the Cramer V statistic was calculated (Fan et al. N Engl J Med. 2006 Aug 10; 355 (6): 560-9).

To measure the discriminative capacity of the five-year model, the corrected Harrell concordance index for bias was calculated. The models were readjusted 500 times with the " bootstrap resampling " technique. The concordance index is the percentage of pairs of patients in which the predicted and observed agree. In this way c = 0.50 represents the concordance due to chance; c = 1.0 represents perfect discrimination (Harrell et al. Stat Med. 1996 Feb 28; 15 (4): 361-87).

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8. Databases for the Validation of the " 8-gene Score "

Four independent data groups available online were used to validate the " 8-gene Score ". These data groups were: NKI, downloaded from the Rosetta Inpharmatics website (http://www.rii.com/publications/2002/
nejm.html), SWE (GSE1456 - Pawitan et al. Breast Cancer Res. 2005; 7 (6): R953-64), UPP (GSE4922 - Ivshina et al . Cancer Res. 2006 Nov 1; 66 (21): 10292 -301) and LOI (GSE6532 - Loi et al . BMC Genomics. 2008; 9: 239). The last three were downloaded from the NCBI GEO data warehouse (http://www.ncbi.nlm.nih.gov/projects/geo/index.cgi). To apply the " 8-gene Score " obtained by qRT-PCR on these microarray data, the expression values were z-transformed. They were then adjusted taking the lowest expression value as 0 and scaled. Normalization by gene was carried out within the validation cohorts using the mean values obtained in the discovery cohort (Schmidt. Cancer Res. 2008 Jul 1; 68 (13): 5405-13).

An analysis of Kaplan-Meier and the groups were compared using the "log-rank" test. An analysis was also applied of the proportional risks of Cox. The assessment criterion was SLRD again.

All statistical analyzes were carried out. carried out using version 9.1 of the SPSS software package, the GraphPad Prism version 5.00 and R version 2.2 with the package Statistical Design version 2.0-12. P values are considered bilateral and statistically significant when p <0.05.

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Results

153 patients diagnosed among February 1995 and March 2003; its clinical features are shown in table 2. The median age was 58 years and the Median follow-up 91 months. A mastectomy was performed on 66 patients (43%), while the rest were operated with conservative surgery followed by adjuvant radiation. All patients received adjuvant tamoxifen for five years and 97 of they (63%) were treated with adjuvant chemotherapy, consistent in the administration of cyclophosphamide methotrexate fluorouracil (CMF) or a similar scheme but including an anthracycline (FAC or FEC). Thirty-four patients (22%) had a relapse to distance, of which 17 died, and there was no follow-up after the relapse of 7 of them. Of the 119 patients without relapse to distance four had local relapse treated with surgery successfully.

TABLE 2 Clinical characteristics of the patients

2

A reduced genetic profile was constructed. First, 53 genes with high correlation between the expression data of the FF and FFPE tissue samples were identified. Subsequently, 17 genes were selected based on their p-value related to SLRD. With these 17 genes, a model was constructed that adequately identified a group of patients at high risk of distant relapse. Models with 10, 9, 8, 7 or even fewer genes also showed a good separation between groups of high and low risk of distant relapse. The 8 gene profile was selected because it was the one that obtained the best performance. The 8 gene profile was calculated in each sample using standardized gene expression measures based on the following equation: " 8-gene Score " = 0.1936 * DTL + 0.2176 * ECT2 + 0.0454 * MTDH + 0.1329 * PRC1 + 0.0556 * RFC4 - 0.1913 * SCUBE2 - 0.0443 * STK32B - 0.1182 * ZNF53. The information
In relation to these eight genes, as well as that of the reference genes, it is encoded in Table 3.

An increase in the expression of DTL genes, ECT2, MTDH, PRC1 and RFC4 is associated with lower SLRD (that is, they are factors that increase the risk of relapse), while an increase in the expression of SCUBE2, STK32B and ZNF533 is associated with greater SLRD (that is, they are protective factors against relapse).

Of the total genes analyzed there are certain genes that coexpress with the predictor components. Due to the Multigenic nature of the predictor, the replacement of these genes by genes that coexpress with them with a correlation coefficient of Pearson r \ geq 0.4 would not alter the discriminative capacity of the predictor These genes are: AYTL2, BIRC5, CCNB1, CCNE2, GMPS, MCM6, MELK, MYBL2, ORC6L, PGR and TGFB3 (Table 4).

TABLE 3 Identification of the genes included in the " 8-gene Score " and reference genes (the asterisk indicates the reference genes).

3

TABLE 4 Identification of genes that can replace those that constitute the " 8-gene Score "

4

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With the intention of assigning each patient to a risk group, a cut-off point was defined at 2.86. Thus, patients with a profile score <2.86 were assigned to the group of low risk of distant relapse and patients with a score ≥2.86 constituted the group of high risk of distant relapse. The p-value of the statistical " log-rank " test among the risk groups based on 2000 permutations was 0.044. This value provided statistical significance to the cutoff point. The " 8-gene Score " assigns patients to the low (60%) and high risk (40%) groups, as shown in Figure 2.

Survival free from distant relapse to five years was 97.7% for the low risk group and 60.6% for the high risk group (HR: 20.4, 95% CI: 6.2 - 67.5; p <0.001). The overall survival at five was also calculated years: 98.9% for the low-risk group of distant relapse and 86.6% for the high-risk group (HR: 7.496, 95% CI: 2.4-23.4; p <0.001).

A sub-analysis was carried out considering the state of the lymph nodes, as shown in Figure 3. In patients with positive lymph node involvement, the " 8-gene Score " included half of the patients in each group (low risk vs high risk): Five-year SLRD was 93.3% vs. 39.5% In women with negative lymph node involvement, the values were 100% vs. 75.7%, respectively.

Cox multivariate analysis included the " 8-gene Score ", tumor size, lymph node involvement status and the degree of tumor differentiation. The " 8-gene Score " is a predictor of SLRD (Table 5), indicating that this gene expression profile adds important prognostic information to that provided by traditional clinical factors. Nodal status is the only traditional clinical factor that maintains statistical significance in this multivariate analysis.

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TABLE 5 Multivariate analysis for the " 8-gene Score ", the " 70-gene Signature " and the " Recurrence Score " (Tumor Size:> 2 cm vs. ≤2 cm. Ganglionic Status: Positive (1-3 affected nodes) vs Negative

5

Cramer's V statistic was used to determine the agreement between the " 8-gene Score " and the other profiles studied. The correlation for the " 8-gene Score " was 0.65 with the " 70-gene Signature " and 0.58 with the " Recurrence Score ". These results indicate a strong correlation between the " 8-gene Score ", the " 70-gene Signature " and the " Recurrence Score ".

To determine the discriminative capacity of each 5-year profile, the corrected Harrell concordance index for bias was calculated. The values were: 0.81 for the " 8-gene Score ", 0.73 for the " Recurrence Score " and 0.70 for the " 70-gene Signature ". These values indicate that the " 8-gene Score " is comparable to the other two profiles in terms of discriminative ability.

The " 8-gene Score " was applied to a database of the Dutch Cancer Institute (NKI) that had previously been used to compare several profiles (Fan et al . N Engl J Med. 2006 Aug 10; 355 (6): 560-9). The " 8-gene Score " showed significant differences in SLRD in the entire population of 295 patients, and also in the groups of positive nodes, negative nodes and positive estrogen receptors (Table 6). In a direct comparison with the " 70-gene Signature ", the " 8-gene Score " assigned more patients to the low-risk group, while the SLRD was slightly lower for all groups. If the cut-off point was modified to include as many patients in the low-risk group as the " 70-gene Signature " does, the results were virtually identical.

The performance of the " 8-gene Score " in three additional databases was analyzed below: SWE, UPP and LOI. Significant differences in SLRD were found in all of them (Table 7 and Figure 4). In all cases, the " 8-gene Score " assigned more than 55% of patients to the low-risk group, with an SLRD above 90% in the LOI and SWE databases and above 80% in the UPP database. .

Differences in ten years SLRD were also significant (data not shown).

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TABLE 6 Comparison between the " 8-gene Score " and other gene profiles using the NKI databases. * Proportion of patients included in the favorable and unfavorable risk groups with each gene profile. ** Low risk + intermediate risk vs high risk

6

TABLE 7 Operation of the " 8-gene Score " in other databases. * SLRD values are 5 years

7

Example 3 Example of use of the " 8-gene Score " in serous ovarian tumors

Serous ovarian tumors account for more than 50% of all ovarian tumors. Approximately 10-15% of these tumors are classified as proliferative tumors or with low malignant potential (LMP, acronym for " Low Malignant Potential "), considering the rest invasive tumors. The ability of the " 8-gene Score " to separate these two populations of serous ovarian tumors has been verified using the GSE12172 database of the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/) . The microarray data were processed following the protocol described for the validation series used in the development of the " 8-gene Score " in Example 2. The database contains 90 samples, of which 30 are invasive LMP and 60 samples . The clinical data does not include the time until relapse, therefore survival studies cannot be done as they were performed for the databases used in the validation. However, as shown in the article by Anglesio and cois. (Anglesio et al . Mol Cancer Res. 2008 Nov; 6 (11): 1678-90), it is possible to discriminate between the two types of tumors. Applying the " 8-gene Score " and performing a Mann-Whitney test, it is concluded that the " 8-gene Score " is able to differentiate serous ovarian tumors with low malignant potential from serous ovarian tumors of the invasive type in this population (figure 5). The results obtained show the diagnostic potential of the " 8-gene Score " in a tumor type different from that in which it has been developed.

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         \ vskip0.400000 \ baselineskip
      

<400> 20

74

75

76

         \ vskip0.400000 \ baselineskip
      

<210> 21

         \ vskip0.400000 \ baselineskip
      

<211> 1647

         \ vskip0.400000 \ baselineskip
      

<212> DNA

         \ vskip0.400000 \ baselineskip
      

<213> Homo sapiens

         \ vskip1.000000 \ baselineskip
      

         \ vskip0.400000 \ baselineskip
      

<400> 21

77

78

         \ vskip0.400000 \ baselineskip
      

<210> 22

         \ vskip0.400000 \ baselineskip
      

<211> 13037

         \ vskip0.400000 \ baselineskip
      

<212> DNA

         \ vskip0.400000 \ baselineskip
      

<213> Homo sapiens

         \ vskip1.000000 \ baselineskip
      

         \ vskip0.400000 \ baselineskip
      

<400> 22

79

80

81

82

83

84

85

86

87

88

89

         \ vskip0.400000 \ baselineskip
      

<210> 23

         \ vskip0.400000 \ baselineskip
      

<211> 3183

         \ vskip0.400000 \ baselineskip
      

<212> DNA

         \ vskip0.400000 \ baselineskip
      

<213> Homo sapiens

         \ vskip1.000000 \ baselineskip
      

         \ vskip0.400000 \ baselineskip
      

<400> 23

90

91

92

93

Claims (17)

1. Method for tumor subclassification, which includes:

to.

obtaining a biological sample isolate comprising tumor cells of the mammal;

b.

product quantity detection of the expression of between two and eight genes being a PRC1 gene in combination with the following genes: DTL, ECT2, MTDH, RFC4, SCUBE2, STK32B or ZNF533, in the sample obtained in (a) and

C.

comparison of the amount detected in step (b) with a reference amount.

2. Method according to claim 1 wherein in the step (b) the amount of the product of the expression of the DTL, ECT2, MTDH, PRC1, RFC4, SCUBE2, STK32B and ZNF533 genes. 3. Method of forecasting the evolution of tumor comprising steps (a) - (c) according to any of the claims 1 or 2, further comprising a step (d) wherein the amount detected in step (b) of the expression product of DTL, ECT2, MTDH, PRC1 or RFC4 genes greater than the amount of reference to the one compared in step (c) or an amount detected in step (b) of the minor SCUBE2, STK32B or ZNF533 genes that the reference quantity with that compared in step (c), It is indicative of a lower relapse-free survival at distance or lower overall survival. 4. Method according to any of the claims 1 to 3, wherein the gene expression product It is the mRNA. 5. Method according to any of the claims 1 to 4, wherein the mRNA detection is performed using the qRT-PCR technique. 6. Method according to any of the claims 1 to 5 where the isolated biological sample comprising cells Tumor is fixed and embedded in paraffin. 7. Method according to any of the claims 1 to 6 wherein the tumor belongs to the following group: breast, gynecological or prostate. 8. Method according to any of the claims 1 to 7 wherein the tumor is breast. 9. Method according to claim 8 wherein the Breast tumor is an infiltrating breast carcinoma. 10. Method according to claim 9 wherein the infiltrating breast carcinoma is in stage I or II. 11. Method according to any of the claims 8 to 10 wherein the tumor is positive for expression of estrogen hormone receptors. 12. Method according to claim 11 wherein the sample is isolated from a mammal that has undergone therapy hormonal and either a mastectomy or a lumpectomy followed by radiotherapy. 13. Method according to any of the claims 1 to 7 wherein the tumor is ovarian. 14. Method according to any of the claims 1 to 7 wherein the ovarian tumor is a tumor epithelial ovary 15. Method according to claim 14 wherein the Epithelial ovarian tumor is a serous tumor. 16. Kit adapted to carry out the method for tumor subclassification, according to any of the claims 1 to 15 comprising a direct primer and a reverse primer that hybridize with the fragment defined between nucleotides 106 and 238 of SEQ ID NO: 4 and the other reagents necessary for:

to.

product quantity detection of the expression of between two and eight genes being a PRC1 gene in combination with the following genes: DTL, ECT2, MTDH, RFC4, SCUBE2, STK32B or ZNF533, in the sample obtained in (a) and

b.

comparison of the amount detected in step (b) with a reference amount.

17. Kit according to claim 16 wherein the Detection of the quantity of the product is the expression of the DTL, ECT2, MTDH, PRC1, RFC4, SCUBE2, STK32B and ZNF533 genes.