MX2008004462A - Method and device for the in vitro analysis of mrna of genes involved in haematological neoplasias - Google Patents

Abstract

The invention relates to a method and device for the in vitro analysis of mRNA of genes involved in haematological neoplasias. The inventive device, which comprises probes which specifically hybridise with genes involved in haematological neoplasias and which are designed such as to perform in a similar manner in the hybridisation, can be used to evaluate the level of mRNA in biological samples taken from subjects suspected of having a haematological neoplasia and to facilitate the comparison of different samples and the grouping of said samples according to similarity in the gene expression patterns, especially when the probes are disposed in the form of a microarray. The inventive method can be used to obtain and process data from the device relating to differences in gene expression such as to identify significant genes in order to distinguish samples that are associated with haematological neoplasias. In addition, the method facilitates the diagnosis of neoplasias such as LLC and the prognosis of the development of same.

Description

PROCEDURE AND DEVICE OF IN VITRO ANALYSIS OF mRNA OF GENES INVOLVED IN HEMATOLOGIC NEOPLASIAS FIELD OF THE INVENTION The invention is assigned to the technical-industrial sector of the in vitro, extracorporeal diagnosis of biological samples, by means of genetic engineering techniques, applied to the diagnosis of specific types of neoplasms from their gene expression patterns and / or to the prognosis of its evolution. More specifically, the invention refers to the identification of neoplasms originated from hematopoietic cells from the evaluation of the messenger RNA levels of significant genes in biological samples such as peripheral blood samples, preferably by means of the use of microarrays. This can identify samples corresponding to patients suffering from CLL, allowing the diagnosis of it and, in addition, samples from patients with CLL can be classified into samples belonging to patients in which the CLL is going to remain stable or in which it goes to progress, allowing the prognosis of the future evolution of these patients. BACKGROUND OF THE INVENTION Each day, the human body produces billions of new white, red and platelet cells that replace hematopoietic cells that are lost as a result of a normal process of renewal, disease or trauma. The organized process of production of haematopoietic cells and homeostasis is known as hematopoiesis (Eissman IL et al., 2000; Leung AYH et al., 2005. In man, hematopoiesis is confined to the bone marrow (BM)). most of the bones, and gradually, with age, this is replaced by fat so that, in the adult, 70% of the bone marrow is located in the pelvis, vertebrae and sternum (Bernard et al., 1976) All mature blood cells are generated from a relatively low number of hematopoietic cells known as stem or hematopoietic stem cells.Hematopoietic stem cell has two characteristics that are pluripotentiality or ability to give rise to different hematopoietic cell lines and the self-renewal or property of perpetuating itself generating cells equal to itself (Weissman IL et al., 2000) .This capacity is essential for the maintenance of hematopoiesis a throughout life since, without self-renewal, the reserve of available stem cells would quickly be exhausted. Hematopoietic stem cells are capable of generating different mature hematopoietic cell types through a series of progenitors and intermediate precursors. These parents and precursors undergo an ordered sequence of events that transform them into mature cells. This process is known as differentiation (Lee MF et al., 2005). The differentiation of hematopoietic cells involves changes that affect, among others, the size and shape of the cell, the expression of genes, proteins, response to signals and localization of cells. The terminally differentiated cells have lost their capacity for division and undergo apoptosis after a period of time ranging from hours for neutrophils to decades for some lymphocytes. This fact makes the M.O. must constantly ensure cell turnover (Datta SR et al., 1999). The process of hematopoiesis involves a complex interaction between intrinsic genetic events of the hematopoietic cells and the environment in which they are found. This interaction determines whether hematopoietic precursors and progenitors must remain quiescent, proliferate, differentiate in one line or another or enter apoptosis (Domen J et al., 1999). All the genetic and environmental mechanisms that govern the production of blood cells operate by altering the relative balance of these fundamental cellular processes. Environmental and genetic factors are critical in hematopoiesis. For example, the expression of genes belonging to the Rb families (Bergh et al., 1999), cyclins (Della Ragione F et al., 1997) or Hox (agli MC et al., 1997) regulate the proliferation of hematopoietic cells in early stages of differentiation. The genes of the bel-2 family regulate apoptosis in hematopoietic cells (O'Gorman DM et al., 2001). A great variety of genes among which are C / EBP (Teñen DG et al., 1997), Pax5 (Nutt SL et al., 1999) and Ikaros (Nichogiannopoulou A. et al., 1998) seem to be involved in differentiation hematopoietic and line commitment. Hematological neoplasms Haematological malignancies are malignant processes that affect any of the cell types involved in the hematopoietic system. As a consequence of this transformation, the cell is blocked in a differentiation stage and begins to accumulate due to an uncontrolled proliferation, a failure of the apoptotic mechanism or a blockage of its differentiation process.

The malignant transformation of the hematopoietic cells during the different stages that they undergo in their differentiation into mature cells originates a large number of different neoplasms (Guttmacher AE et al., 2003). This type of neoplasms is therefore a very heterogeneous group of diseases that only have in common the hematopoietic origin of the transformed cell type. Classification of hematological neoplasms Generically, two large groups could be established: the lymphoid neoplasms that affect the different cell types and maturational degrees that make up the B and T lymphoid line and the other large group are the myeloid neoplasms that affect various cell types of the myeloid line. However, this simplistic classification is currently more developed, as detailed below. From the clinical point of view, the leukemias of lympholas have traditionally been arbitrarily differentiated, pointing to leukemias as those neoplasms that affect the bone marrow and have peripheral expression, that is, circulation of abnormal cells in blood, and lymphomas such as those neoplasms that remain located in the lymph nodes or other lymphoid tissues and that lack, at least initially, leukemic behavior. In the case of leukemias, acute and chronic processes have been differentiated initially by the morpho-cytological characteristics of the proliferating cells (immature and atypical in the first case and more differentiated in the second) and the clinical manifestations of the disease. At present, the knowledge of the immunological markers and the genetic alterations that affect the hematopoietic cells help to differentiate with more precision the different processes. Today it is known that hematological neoplasms, as occurs in other types of cancer, have a multigenic origin. The great technological revolution produced in recent years has meant knowing the molecular basis of several neoplasms. The use of these techniques allows to identify relevant genes in human cancer, confirm the results obtained in basic research in animal models, establish patterns of susceptibility, classify more accurately the neoplasms, improve the diagnosis of the disease, identify new therapeutic targets and improve the therapeutic selection for each patient. Also the diversity that exists between individuals is important and has its clinical repercussion, based on genetic differences: if we are able to recognize these genetic differences we will also be able to advance in knowing toxicity and differences in the response to treatment. (Westbrook CA et al., 2005). In 1995, the World Health Organization (WHO / WHO) in collaboration with the European Association of Hematology and pathologists, clinicians and scientists from around the world, initiated a project with the aim of obtaining a consensual classification of hematopoietic tissue tumors. and lymphoid organs. This project led to the development of a system for the definition, classification and establishment of consensus diagnostic criteria for myeloid, lymphoid and histiocytic neoplasms (Jaffe ES et al., 2001). The WHO classification criteria are the same as those used in the REAL classification (European-American Revised Classification of Lymphoid Neoplasms) published by the International Lymphoma Study Group in 1994 (Harris NL et al., 1994). The REAL classification system, unlike other previous classification systems, is based on the definition of "real" entities and not on morphological subtypes. For the establishment of these "real" entities, all the available information is used, that is, morphological, immunophenotypic and biological data are combined with the genetic and clinical characteristics (Harris NL et al., 1999a). The classification of O S, which was presented in 1997, stratifies the entities according to the affected cell line: myeloid, lymphoid, histiocytic / dendritic and mastocytic. Within each category, the disease is defined according to morphology, immunophenotype, genetic and clinical data (Harris NL et al., 1999b). In many neoplasias, the stage in which the accumulated tumor cell is located does not coincide with the stage in which the initial transforming event has occurred. Thus, many hematological neoplasms originate in the initial precursors and the specific genetic alteration may determine that the cell continues to advance in its differentiation until it stops and accumulates in more advanced stages of differentiation (Shaffer AL et al., 2002). On the contrary, other neoplasms can develop in more advanced stages of differentiation, such as occurs in cells from the follicular center in which translocations and genetic rearrangements produce activation of genes that contribute to tumor development. The nomenclature for each entity reflects the best estimate for its cell line and stage of differentiation, recognizing that the knowledge currently available is imperfect and that changes in the assignment to the cell line and in the nomenclature may occur as the knowledge available The current criteria for diagnosis and classification of these neoplasms are based on a combination of (Braziel RM et al., 2003): Morphological evaluation of the cell: Microscopic observation of the cells involved. Information is obtained about the type of cell and degree of maturation in which it is found. Immunophenotype study: Recognition of antigens expressed on the surface of the neoplastic cell. These antigens are expressed differently and to a different degree depending on the lineage and the stage in which the cell is located. The expression of surface antigens characteristic of the line and stage of differentiation in which the cell is found is known, for example, the expression of CD19 and CD20 is typical of B-line cells, while the expression of CD3 is typical of line T. The study of CD23 is key when differentiating LNHC from LLC (Gong JZ et al., 2001).

Historically, we have tried to relate the different types of neoplasms with their corresponding normal cell population through their morphological and immunophenotypic characteristics. Many neoplasms therefore appear to be "trapped" in determined stages of development because they present morphological and immunophenotypic characteristics similar to those presented by the hematopoietic cell in that stage of differentiation (Shaffer AL, et al., 2002). Clinical characteristics: Signs and symptoms that the patient presents at the time of diagnosis. Determination of molecular markers: Measurement of some molecules that are associated with specific entities such as the presence of PML / RARA in promyelocytic leukemia or that confer better or worse prognosis, such as the expression of CD38 in LLC cells marker of poor prognosis (Durig J et al., 2002) Cytogenetic studies based on the search for genetic alterations in the DNA of tumor cells. In many cases, there are specific rearrangements characteristic of tumor types or stages (Mitelman F, et al., 1991). Depending on the chromosomal translocations, different groups with clinical significance can be established, for example, in LLA-B, where the presence of fusion oncoproteins is frequent, the presence of t (2; 21) / TEL1-AML1 and t (1; 19 ) / E2A-PBX1 is associated with response to treatment while the prognosis for patients with t (9; 22) / BCR-ABL and t (4; 11) / MLL-AF4 is much worse (Arico M et al., 2000) . There are also frequent searches for point mutations, deletions or insertions in some gene that have been linked to a more favorable prognosis, such as the myelodysplastic syndromes associated with 5q- (Boultwood J et al., 1994).

As previously mentioned, the WHO establishes four large groups of hematological neoplasms according to the lineage involved (myeloid, lymphoid, histiocytic / dendritic and mastocytic neoplasms). The neoplasms belonging to the myeloid and lymphoid lines are described more extensively, since they are the ones that occur most frequently. Those corresponding to the histiocytic / dendritic and mastocyte lines at the moment constitute very specific entities. 1. Myeloid neoplasms These group together all the neoplasms originating in the myeloid differentiation line; WHO distinguishes four large groups (Vardiman JW et al., 2002). 1. 1 Myeloproliferative syndromes (SMP) Myeloproliferative syndromes (SMP) are clonal alterations of the hematopoietic stem cell characterized by effective hematopoiesis that leads to an increase in blood levels of one or more hematopoietic lines and hepatosplenomegaly. They constitute a group of entities in which there is an increase in the precursors of myeloid series or fibrosis of the bone marrow (myelofibrosis), this group also includes systemic mastocytosis. They stand out: Chronic myeloproliferative syndromes (SMPC). Clonal alteration of the hematopoietic stem cell. Characterized by an effective hematopoiesis that produces increase in peripheral blood of one or more cell lines and frequently hepatosplenomegaly, medullary hypercellularity with maturation but without dysplasia.

- Chronic myeloid leukemia (CML). It is a clonal process secondary to an acquired genetic alteration of the pluripotent cell. The disease is characterized by the overproduction of neutrophils and their precursors. It has three phases: the first called the chronic phase of undefined duration, followed by the acceleration phase and finally the blast crisis, which is really a secondary acute leukemia. L C has a low incidence of approximately one case per 100,000 inhabitants / year and appears more frequently in the fifth and sixth decade of life. It can be considered a rare disease. It is the characteristic leukemia par excellence since the term leukemia was applied for the first time in this entity. 95% of the cases present a genetic marker, the Philadelphia chromosome, originated by the translocation of a fragment of chromosome 22 that sticks to chromosome 9 or t (9; 22) (q34, -qll). This translocation originates the bcr-abl fusion gene. The protein encoded by this chimeric gene, BCR-ABL has an increased tyrosine kinase activity compared to the activity of the normal abl protein and acts as an oncogenic growth factor (Pane F et al., 2002), although the mechanisms that produce the overproduction of myeloid cells is not completely clear. It is possible that other proto-oncogenes such as p-53 are involved in the process and in the transformation from chronic phase to blastic crisis. The few cases in which the presence of the Philadelphia chromosome is not detected represent atypical myeloproliferative conditions and correspond to the variant MDS known as chronic myelomonocytic leukemia (CMML). The diagnosis is based on elevated cell counts for the white series, appearance of normal myeloid cells morphologically and in all stages of differentiation, but with a high number of myelocytes and neutrophils, usually there is basophilia and thrombocytosis. In the acceleration phase there is an increase of immature cells in peripheral blood and in the blast crisis the predominant cell is myeloblast (65%) or lymphoblast (35%).

Polycythemia Vera (PV). It is the myeloproliferative syndrome characterized by the increase in the mass of the red series. Polycythemia vera is a benign hematological disease, whose condition does not influence the shortening of survival. However, it is a clonal disease that can progress in 15% of patients to myelofibrosis or acute leukemia (5%).

Essential thrombocythemia (TE). Myeloproliferative syndrome characterized by a platelet production 15 times higher than normal. It can be associated with thrombotic or hemorrhagic complications secondary to platelet dysfunction. The age of presentation is around 60 years, with equal incidence in both sexes. - Myelofibrosis (MF). It is a neoplastic clonal disorder of the pluripotent stem cell. It is characterized by a large production of abnormal megakaryocytes. These cells release molecules (platelet-derived growth factor, platelet factor 4) that stimulate the proliferation of fibroblasts and build collagen fibers in the bone marrow. The marrow is unable to function normally and the hematopoietic precursor cells move to the liver and spleen, leading to extramedullary hematopoiesis. Characterized by M.O fibrosis and splenomegaly. Appears in over 50 years and has no preference for sex.

Mastocytosis Group of entities characterized by the proliferation of mast cells in different parts of the body. Systemic mastocytosis (MS) is a rare disease that usually affects adults, and presents bone alterations in 70% of patients (Chen CC et al., 1994). 1.2. Myelodysplastic / myeloproliferative syndromes (S D / SMP) WHO has established a somewhat different classification, separating MDS / SMP as differentiated entities from the rest of MDS, since they share characteristics with SMPC that make them different. This group includes three entities: chronic myelomonocytic leukemia, atypical chronic myeloid leukemia, juvenile myelomonocytic leukemia, and unclassifiable SMD / SMP. Myelodysplastic syndromes (MDS) are clonal proliferations of the hematopoietic stem cell that share clinical, morphological and analytical data that overlap between LAM and SMPC at the time of diagnosis. They are characterized by the hypercellularity of the bone marrow due to the proliferation of one or more myeloid lines (Heaney ML, 1999). The presence of dysplasia in at least one line (myeloid, erythroid or megakaryocytic-platelet) is a characteristic of MDS. The incidence is variable depending on the variety. An incidence of 3 cases per 100,000 inhabitants over 60 years of age is estimated. The FAB classification establishes 4 diagnostic categories (Bennett JM et al., 1984): simple refractory anemia (RA), refractory anemia with ring sideroblasts (ARS), refractory anemia with excess blasts (AREB) and refractory anemia with excess blasts in transformation (AREB-T) and chronic myelomonocytic leukemia (CMML).

With regard to MDS, WHO establishes five differentiated categories (Harris NL, et al., 1999): refractory anemia, refractory cytopenia with multilinear dysplasia, refractory anemia with excess blasts, unclassifiable MDS and SMD associated with defect isolated on chromosome 5 (of 5q) or 5q- syndrome. 1.3. Acute myeloblastic leukemia (AML) Clonal proliferation of immature cells of the myeloid line. They may appear de novo or secondarily in patients with myelodysplastic syndrome (MDS). The classification developed by the French-American-British group (FAB) considers eight varieties (M0-M7) based on morphological criteria and the immunophenotype of neoplastic cells (Bennett JM, et al., 1976). Although this classification has been accepted for many years, the discovery that many genetic alterations have a predictive nature and the incorporation of cytogenetic analysis to the diagnosis of acute leukemia (Bene MC et al., 2001) has allowed to sublite the disease and establish a prognostic assessment, as occurs with the translocation t (15; 17) which characterizes the acute promyelocytic leukemia variety that is characterized by the expression of a receptor for retinoic acid (RAR), a characteristic that makes this type of leukemia sensitive to treatment with transretinic acid (ATRA) in most the cases. The WHO classifies LAMs incorporating morphological data, immunophenotypic, genetic and clinical characteristics in order to define biologically homogeneous entities with clinical relevance. Thus the LAM is classified into four major categories: 1.- LAM with recurrent genetic anomalies. 2.- LAM with multilinear dysplasia. 3.- LAM related to treatment and 4.- LAM not classifiable (ref WHO). The first three categories recognize the importance of biological factors that predict the evolution of the process. Cytogenetic analysis represents the most powerful prognostic factor (Roumier C, et al., 2003). It is used to identify subgroups of LAM with different prognosis: low risk with favorable response to treatment (t (8; 21), t (15; 17) or inv (16)), intermediate risk (normal karyotype ot (9; ll) or high risk (inv (3), -5del (5q) or -7del (7q), or more than three alterations.) There is molecular heterogeneity within the risk group.In some cases of patients with normal karyotype, the presence of mutations in the FLT3 gene (Kottaridis PD, et al., 2001.) and MLL (Dohner K et al., 2002).

The medullary image in the microscopic examination of the aspirate is generally that of an invasion by cells similar to each other, of immature morphological characteristics that distort the normal cellular distribution constituting authentic cellular sheets. The medullary hyperproduction determines that areas of inactive bone marrow in adulthood return to present foci of hematopoiesis, in this case of abnormal cells. Approximately 80-90% of young patients with LAM reach complete remission of the disease after chemotherapy. However, the majority relapses, and healing occurs at 30%. The allogeneic bone marrow transplant has managed to increase the cure rate to 50%, but is limited by the availability of identical HLA donor. It is therefore a group of neoplasms with diverse genetic abnormalities and response to variable treatment (Giles FJ et al., 2002) 2. Lymphoid neoplasms The WHO classification is a refinement of the REAL classification (Harris NL et al., 1994) . Three large groups of lymphoid neoplasms are recognized: 1.- Lymphoid neoplasms derived from B cells. 2.- Lymphoid neoplasms derived from T and NK cells. 3.- Hodgkin's lymphoma. This classification includes solid neoplasms and lymphoid leukemias because in many of them there is a transformation from one phase to another and the distinction between them can be artificial. Thus, chronic lymphocytic leukemia B and lymphocytic NHL are caused by the same cell and simply represent different manifestations of the same neoplasm, the same occurs with lymphoblastic lymphoma and lymphoblastic leukemia. 2.1. Neoplasms derived from B, T and NK cells The WHO classification divides these neoplasms according to the stage of maturation in which the cells are found in neoplasms of precursor cells and neoplasms of mature cells (WHO Classification Tumors of Haematopoietic and lymphoid tissues. In Pathology and genetics of tumours of Haematopoietic and lymphoid tissues ES Jaffe, NL Harris, H Stein, JW Vardiman, IARC Press, Lyon, 2001). Due to the high number of entities described, the following stand out among them: Acute lymphoblastic leukemia (LAL): Clonal profile of lymphoid precursors. In approximately 80% of cases, the precursors belong to the B lymphoid line. Molecular analysis of the genetic alterations of leukemic cells has contributed significantly to the understanding of the pathogenesis and prognosis of LAL (Ferrando AA et al., 2005). Although the frequency of genetic subtypes differs in children and adults, the general mechanisms that lead to LAL are a consequence of the abnormal expression of proto-oncogenes due to chromosomal translocations that create fusion genes or hyperploidy. This initial oncogenic event is probably insufficient to produce leukemia and it is believed that other alterations are necessary that cooperate with this first to definitively alter the proliferation and survival of the transformed cell. All these alterations contribute to the leukemic transformation of the mother hematopoietic cells or their progenitors because they affect key regulatory processes, maintaining or increasing their capacity for self-renewal, escape from normal proliferation controls, blocking differentiation and promoting resistance to apoptotic signals ( Hanahan D, et al., 2000).

The overall appearance of the bone marrow is similar to that described for myeloid leukemia. The investigation of minimal residual disease is important, a factor that conditions the probable relapse of the disease with its presence. The FAB classification defines 3 stages according to the morphology (L1-L3).

It is the most frequent leukemia in childhood, and in the clinical course and the response to treatment depend on the type of genetic alteration, for example, patients with hyperdiploidy have a favorable prognosis when treated with treatment schemes that include antimetabolites but in general children are cured with standard chemotherapy and CNS prophylaxis and in adults only 20% have prolonged survival with chemotherapy, allogeneic and autologous transplantation is useful for cases considered high risk. Chronic lymphatic leukemia (CLL). CLL is characterized by clonal proliferation and accumulation of lymphocytes with mature appearance and resistant to apoptosis in M.O, blood and lymphoid organs (Galton DA, 1966). When lymphadenopathy is dominant, the clinical picture is called lymphocytic lymphoma. Affected lymphocytes are B-lined in 95% of cases and 5% of cases involve T-lymphocytes. It is the most common leukemia in the Western world. The average age of patients at diagnosis is 65 years, only 10-15% of cases are below 50 years (Jemal A et al., 2003 J. It is the most common cause of leukemia in adults in the countries from the western world and accounts for about 25% of all leukemias, incidence is 3 cases per 100,000 inhabitants per year, with a predominance in males, with a male / female ratio of 1.7: 1. In recent years , it is increasingly diagnosed in younger patients.The proportion of cases diagnosed in the early stage of the disease (Rai KR, et al., 1975) has increased from 10 to 50%, probably due to an early diagnosis thanks to Routine lymphocytic counts The disease affects men more than women The prognosis and clinical course of the disease is extremely variable Some patients have a rapidly progressive evolution and die within 2-3 years after diagnosis, while in others,The course is indolent and they live for 10-20 years without problems related to the LLC. Intermediate situations occur in half of the patients. Approximately 20% of patients are asymptomatic at the time of diagnosis, as a result of a routine blood test. When there are symptoms, they are not specific and include fatigue, weakness and discomfort. The Binet classification (Binet JL et al., 1981) defines 3 stages of disease depending on the concentration of hemoglobin, number of platelets, number of lymph nodes involved and the presence of visceromegalies. The Rai classification (Rai KR et al., 1975) uses the same indicators but classifies patients into five groups. This neoplasm is not characterized by a single and recurrent genomic alteration. There are some markers that print a more unfavorable prognosis such as the presence of deletions on chromosomes 17 and 11 and those patients with absence of mutations in genes IgVh (40% of cases) and high proportion of cells expressing CD38 are characterized by a course more aggressive clinical and a worse response to treatment (Hamblin TJ et al., 1999; Durig J et al., 2002). Another recently described marker is ZAP-70, an independent prognostic marker whose expression is indirectly related to the mutational state of the immunoglobulin heavy chain gene (Crespo M et al., 2003).

- Multiple myeloma: MM). M is a malignant disease in which a clone of plasma cells (terminal element of the B lymphoid line) of the bone marrow undergoes an uncontrolled proliferation. It accounts for 10-15% of all malignant diseases and is characteristic of advanced ages, only 2% of cases are diagnosed before the age of 40. For unknown reasons the incidence of the disease is increasing. These cells produce and secrete a monoclonal immunoglobulin or fragments of immunoglobulins, composed of a heavy and light chain class (kappa or lambda). Occasionally the myeloma may not secrete or the protein in serum or urine may not be detectable. The neoplastic plasma cell produces other molecules such as IL6, tumor necrosis factor or osteoclast activating factor that contribute to osteolysis, hypercalcemia and renal failure, characteristic alterations of the disease. The diagnosis can be casual when performing an analysis in patients without symptoms or limited disease (20% of cases). The disease in these patients can remain stable for years and early treatment in the asymptomatic phase provides no advantage. Patients with a monoclonal component but who do not meet the diagnostic criteria for M are considered carriers of monoclonal gammopathy of undetermined significance (MGUS). Between 10 and 20% of these patients develop MM in 10 years (Kyle RA, 1997, Zhan F et al., 2002). The monoclonal component can also be associated with other diseases such as lymphoma, non-haematological neoplasms and connective tissue diseases.

Lymphoplasmocytic lymphoma and Waldenstrom's macroglobulinemia. It is the clinical expression of a low-grade lymphoproliferative disease, characterized by the infiltration of abnormal lymphoplasmacytic cells in bone marrow, lymph nodes and spleen, accompanied by the monoclonal production of immunoglobulin, which causes an increase in blood viscosity and the appearance of hemorrhagic vascular manifestations and by difficulty in circulation in small vessels.

- Non-Hodgkin lymphoma (NHL). NHL are solid lymphoid tissue tumors that are much more heterogeneous than Hodgkin's disease. The complexity and diversity presented by NHL in terms of morphology, genetics, phenotype and clinical behavior has led to the existence of multiple classifications, none of them completely satisfactory. It is the most frequent hematological disease and, in terms of years of life lost, it is the fourth most important neoplasm in the western world and it seems that its incidence is increasing.

It can appear in all ages, but the median of appearance is at 50 years. The cause that motivates the disease is not clear. Specific chromosomal translocations associated with certain types of lymphoras have been described, so they are very useful in diagnosis (Montoto S et al., 2003). Most Burkitt-type lympholas show the t (8; 14) translocation, in which the c-MYC oncogene on chromosome 8 is transferred to the nearby region on chromosome 14 where the immunoglobulin heavy chains are encoded. Ninety percent of the follicular lymphocytes are characterized by the t (14; 18) translocation, where the bcl-2 gene of chromosome 18 is transferred to the region of the immunoglobulin heavy chains. It is well known that the overexpression of bel -2 inhibits apoptosis (programmed cell death). It is easy for these chromosomal rearrangements to require other stimuli such as the coexpression of a second proto-oncogene or an antigenic stimulus to develop malignant proliferation. An example of a combination of multiple causes combined is AIDS-associated lymphoma. The appearance of aggressive extranodal lymphomas is the result of the combination of immunosuppression by HIV, dysregulation of a protooncogene (c-MYC) and a secondary viral infection (Epstein-Barr virus), the same occurs in patients undergoing organ transplantation (Harris NL et al., 2001).

The clinical presentation of the disease is more irregular than in Hodgkin's disease. It can behave indolently without requiring immediate treatment or, on the contrary, behave aggressively quickly fatal. The most frequent nodal involvement is the cervical. With regard to extranodal involvement, the signs and symptoms depend on the affected organ. Bone marrow appears infiltrated more frequently in low-grade NHL and can cause pancytopenia. The presence of malignant cells in peripheral blood is also common in low-grade NHL, but it is very poor prognosis in high-grade.

The diagnosis is made through the histological study of the lymphatic tissue. Additional information is obtained by monoclonal antibodies directed against specific lymphocyte antigens (immunophenotype); this helps to identify the degree of maturation of the malignant cell and determine the origin T or B of it. The presence of a mutation in genes encoding Ig in B-line NHL is often used for the identification of some subtypes of NHL (Kuppers R et al., 1999). 2.2. Hodgkin lymphoma (LH) It is a rare disease and has a predilection for male sex in a 2/1 proportion. It is characterized by the presence of large, bi or multinucleated cells called Reed-Sternberg (RS) and other smaller and mononuclear cells that appear in small amounts in the tumor, the rest of the cells are lymphocytes, granulocytes, fibroblasts and plasma cells. . This inflammatory infiltrate probably reflects the immune response of the host with the malignant cells. The nature of RS and Hodgkin cells has been well studied but remains controversial. They can be derived from an initial stage of the lymphoid cells. In some cases the existence of Epstein-Barr virus DNA has been detected in the tumor. One hypothesis is that the bimodal distribution of the disease would be due to infection in young subjects and the other peak would be caused by environmental causes. The diagnosis is obtained by biopsy of a lymph node. To plan the treatment it is necessary to determine the extent of the disease. (Küppers R, 2002, Cossman J, 2001, Devilard E et al., 2002). Problems in the classification The large number of hematopoietic cells and the many stages of differentiation they go through further complicates the classification of neoplasms originated from this type of cells. Despite efforts to establish a classification based on "real" entities, some of the established categories are ambiguous and in many cases contain very heterogeneous groups in terms of response to therapy or clinical course. This heterogeneity is responsible, on the one hand, for the relentless search for markers capable of differentiating behavior from others and, on the other hand, for the controversial classification of this type of neoplasm to be subject to continuous revisions. An ideal classification system must be precise, reproducible, easy to use and above all it must have biological and clinical meaning (Chan WC, et al., 2005). The current diagnostic systems and the classification of hematological neoplasms are based on the recognition of histological and morphological characteristics, immunophenotypic, cytogenetics and the study of some molecular marker with prognostic value. A combination of pathological classification and clinical criteria are used to differentiate different types with prognostic differences. However, in some of the diagnostic categories defined in this way it is observed: A markedly heterogeneous response to therapy. Within the same disease are patients who achieve complete remission, partial remission, do not respond, who relapse after a certain therapy. The ability to predict response is especially important in this type of neoplasms since the transplantation of stem cells is an effective but toxic therapeutic alternative. The ability to determine which patients will respond to conventional therapy before giving it may be beneficial to apply the most effective treatment to each patient. - A variable clinical behavior. Within the same category are patients whose disease will remain stable for a long period of time and who will not require therapy and others whose disease will progress rapidly requiring aggressive therapy. These variations point to the existence of molecular heterogeneity within the diagnostic categories, differences that conventional diagnostic methods are not able to determine and hence, the search for new forms of analysis that provide greater resolution in the characterization of this type of neoplasms. In this line, the use of expression "arrays" has been shown to be effective not only in the deciphering of the biological and clinical diversity found in many tumors, but in the understanding of the biological and pathological processes that affect many systems and in particular to the hematopoietic system. The expression "arrays" are ordered sets of sequences associated with a solid support, complementary to mRNA or to their corresponding cDNA or cRNA, which allow the analysis of the differential expression of hundreds or thousands of genes simultaneously. One of the supports to which these sequences frequently link is rectangular glass fragments similar to slides, a format that is often referred to by the terms "microarray", "biochip" or, simply, "chip". Its use is becoming more frequent for the diagnosis of various diseases or for the evaluation of the susceptibility to suffer them. First works of "arrays" and hematological malignancies In 1999, the Golub group published one of the first articles in which reference is made to the role of "arrays" in the classification of hematological neoplasms (Golub TR et al., 1999). An "array" with 6817 genes was used for the study of expression profiles in the LAM and LAL leukemias. A group of 50 genes with the ability to predict the type of leukemia ("class predictor") were selected and used to classify a group of unknown samples in the correct categories. The study of the expression of these 50 genes is sufficient for the classification of a sample of acute leukemia in LAM or LAL. Although the distinction between LAM and LAL is well established with current diagnostic methods, the study revealed the existence of specific expression patterns associated with each type of acute leukemia and evidenced the use of expression profiles in the cancer classification. . In the year 2000, the group of Alizadeh published an article in which using a specialized "array", the "lymphochip" that contains genes expressed preferentially in lymphoid cells or of which some immunological or oncological importance is known with 17,856 sequences (Alizadeh AA et al., 1999). This group used the "lymphochip" for the study of gene expression patterns associated with differences in clinical behavior in a diffuse large B-cell lymphoma (LDCGB) (Alizadeh AA, et al., 2000). The LDCGB is a NHL with a very heterogeneous behavior and impossible to distinguish by conventional methods of diagnosis: 40% of patients respond well to therapy and have prolonged survival while 60% die from the disease. The authors found that gene expression could be related to the clinical behavior of tumors. This was one of the first articles in which the role of "arrays" for the "subclassification" of hematological neoplasms is discussed, that is, the use of expression profiles for the identification of two different LDCGB groups from the point of transcriptional view, subtypes of LDCGB with clinical behavior impossible to predict with conventional diagnostic criteria. At present there are multiple publications in which directly or indirectly the "arrays" appear, applied not only to classification and subclassification, but also to the study, diagnosis, prognosis, identification of new markers in hematological diseases (Greiner TC, 2004; Alizadeh AA et al, 2000; Bea S et al., 2005; Dave SS et al., 2004), as well as patent applications describing the use of this type of device for the differentiation between different types of hematological neoplasms. Thus, for example, patent application WO2003 / 008552 describes the use for diagnostic purposes of differences in the gene expression pattern to differentiate between mixed lineage leukemia (MLL), acute lymphoblastic leukemia (ALL) and myelogenous leukemia. acute (AL), defending the possibility of making this differential diagnosis with the data obtained after the analysis of samples of patients afflicted with each of these types of leukemia through the use of Affymetrix commercial "chips". Although genes are indicated with variations in expression between the three types of leukemia that would allow differentiation between them, specific sequences different from those present in the Affymetrix chip that could be used to detect these genes by devices different from those of said commercial house, nor is considered the design of devices or methods that allow the diagnosis of other types of leukemias or, in general, neoplasms derived from hematopoietic cells.

The patent application WO2005 / 024043, meanwhile, is also framed in the field of gene expression analysis to deepen knowledge of the differences at the molecular level between the different neoplasms derived from hematopoietic cells, focusing specifically on the case of lymphomas, to extract data that help in its diagnosis or in the prognosis of its evolution. Specifically, we describe a method to obtain useful functions to predict the evolution of individuals affected by different types of lymphomas, evaluating in lymph node biopsies to what extent they contribute what they call gene patterns or fingerprints, groups of genes that are They express in a coordinated manner and are related to the cellular origin of the neoplasm, the different types of non-malignant cells present in the biopsy and the oncogenic mechanisms responsible for cancer. The different patterns or gene tracks are also deduced in this case from the data obtained with Affymetrix commercial chips. In addition, the application WO2005 / 024043 claims to provide an alternative microarray, composed of a smaller number of sequences than the Affymetrix microarrays, which would also allow the analysis of differences in gene expression between lymphomas and their application for the deduction of prediction functions of survival and for the differentiation between different types of lympholas. Although the genes whose analysis would be made possible by that microarray are indicated, the memory of the application WO2005 / 024043 does not indicate the sequence of the probes that would make up the microarray, mentioning only that they would be cDNA type and leaving doubts as to whether that cDNA would appear complete or the analysis of the expression of the corresponding gene would be carried out using only a fragment of said cDNA as a probe, which would remain to be determined. It would be interesting to have compositions and methods that allow the differentiation between neoplasms of hematopoietic origin based on their differences at the molecular level, designed specifically for this group of neoplasms, in which the expression of a smaller number of genes than in the commercial microarrays used in the studies described in the aforementioned patent applications and that would facilitate both the diagnosis of certain neoplasms and the prediction of their future evolution, thus helping in the prescription of an appropriate treatment for each patient, a feature particularly interesting in those neoplasms, as in the case of CLL, in which the prognosis of the future evolution of the patient is difficult with the knowledge and trials that are available up to now. Furthermore, it would be especially convenient if the probes used to evaluate the expression of the selected genes had been specifically designed so that, in addition to being specific and with a perfectly defined sequence, they all had a similar behavior, which would make them appropriate, in general , to be used in combination in the same test and, in particular, to be part of the same array or ordered set associated with a solid support, such as the so-called "chips" or "microarrays". The compositions and methods of the present invention respond to this need. Instead of starting from commercial microarrays to detect significant genes to distinguish between neoplasms or create functions that predict the survival of the individual who suffers them, the invention provides new oligonucleotides, of perfectly defined sequence, capable of detecting in a specific way genes that have been selected because they are known to be significant for the biology of blood cells or for the pathology of different neoplasms, oligonucleotides that also have the peculiarity of having been designed in such a way that they share common characteristics that cause them to behave similarly when used as probes in hybridization assays, which makes them suitable for use in compositions comprising combinations thereof. Said compositions, and especially those in which these oligonucleotides are arranged in an orderly manner on a solid, easy-to-use support, such as slide-like glasses, are suitable for carrying out tests with which to detect statistically significant genes to differentiate samples taken from individuals. that suffer from certain types of neoplasms originated from hematopoietic cells of samples taken from individuals who do not suffer from such neoplasms, since they are compositions containing a number of oligonucleotides lower than the commercial microarrays designed for a more general purpose, being designed specifically for the analysis of samples from individuals suffering from neoplasms and composed of probes of known sequence, perfectly reproducible, which are also specially designed to be used together in the same test for being of similar behavior. The additional inclusion in the microarrays of the invention of oligonucleotides of low homology with human genes, but chosen so that the rest of their characteristics are similar to those of the oligonucleotides of the invention designed to act as probes capable of recognizing human genes with high specificity , allows the use of said microarrays for the identification of statistically significant genes in the identification of samples associated with certain neoplasms of hematopoietic origin through the use of trials in which it is feasible to establish controls in all its phases. As demonstrated in the examples that appear hereinafter, the use of these microarrays in combination with various statistical techniques allows the correct classification of different biological samples by a method, precise, reproducible, easy to use and with biological significance and clinical, being based on gene expression differences with meaning for the biological processes that are being analyzed. In particular, the use of a microarray of the invention in combination with the method of the invention allows the identification of blood samples from patients suffering from chronic lymphatic leukemia (alteration not considered in applications O2003 / 008552 and WO2005 / 024043 and whose diagnosis is not has been described using commercial microarrays), distinguishing them both from samples obtained from healthy individuals and from samples related to other types of leukemias, such as those corresponding to Jurkat or U937 cells, facilitating the diagnosis of CLL by analyzing levels of expression of genes statistically significant for this and even allowing the obtaining of functions that enable the mathematical calculation of the probability that an unknown sample belongs to an individual afflicted with CLL. Furthermore, the invention makes it possible to differentiate samples belonging to individuals afflicted with stable chronic lymphatic leukemia from samples belonging to individuals afflicted with progressive chronic lymphatic leukemia, a distinction that is now difficult to carry out a priori by means of the available techniques, which is why it is a useful and useful tool. novel for the prognosis of the future evolution of individuals afflicted with this disease, individuals whose diagnosis may have been made also by compositions and methods of the invention or may have been known through the application of a different procedure, but for which, when available a tool to predict how the CLL they suffer will evolve, it will be easier to decide if it is appropriate to subject them to immediate aggressive treatment or simply keep them under regular observation to verify that their gene expression data continue to indicate that The disease will remain stable for a long period of time. SUMMARY OF THE INVENTION The invention provides compositions that include at least one oligonucleotide from the group consisting of: SG1, SG2, SG3, SG4, SG5, SG6, SG7, SG8, SG9, SG10, SG11, SG12, SG13, SG14, SG15, SG16, SG17, SG18, SG19, SG20, SG21, SG22, SG23, SG24, SG25, SG26 , SG27, SG28, SG29, SG30, SG31, SG32, SG33, SG34, SG35, SG36, SG37, SG38, SG39, SG40, SG41, SG42, SG43, SG44, SG45, SG46, SG47, SG48, SG49, SG50, SG51 , SG52, SG53, SG54, SG55, SG56, SG57, SG58, SG59, SG60, SG61, SG62, SG63, SG64, SG65, SG66, SG67, SG68, SG69, SG70, SG71, SG72, SG73, SG74, SG75, SG76 , SG77, SG78, SG79, SG80, SG81, SG82, SG83, SG84, SG85, SG86, SG87, SG88, SG89, SG90, SG91, SG92, SG93, SG94, SG95, SG96, SG97, SG98, SG99, SG100, SG101 , SG102, SG103, SG104, SG105, SG106, SG107, SG108, SG109, SG110, SG111, SG112, SG113, SG114, SG115, SG116, SG117, SG118, SG119, SG120, SG121, SG122, SG123, SG124, SG125, SG126 , SG127, SG128, SG129, SG130, SG131, SG132, SG133, SG134, SG134, SG136, SG136, SG137, SG138, SG139, SG140, SG141, SG142, SG143, SG144, SG145, SG146, SG147, SG148, SG149, SG150, SG151 , SG152 SG153, SG154, SG155 SG156, SG157, SG158, SG159, SG160 SG161, SG162, SG163 SG164, SG165, SG166, SG167, SG168 SG169, SG170, SG171 SG172, SG173, SG174, SG175, SG176 SG177, SG178, SG179 SG180, SG181, SG182, SG183, SG184 SG185, SG186, SG187 SG188, SG189, SG190, SG191, SG192 SG193, SG194, SG195 SG196, SG197, SG198, SG199, SG200 SG201, SG202, SG203 SG204, SG205, SG206, SG207, SG208 SG209, SG210, SG211 SG212, SG213, SG214, SG215, SG216 SG217, SG218, SG219 SG220, SG221, SG222, SG223, SG224 SG225, SG226, SG227 SG228, SG229, SG230, SG231, SG232 SG233, SG234, SG235 SG236, SG237, SG238, SG239, SG240 SG241, SG242, SG243 SG244, SG245, SG246, SG247, SG248 SG249, SG250, SG251 SG252, SG253, SG254, SG255, SG256 SG257, SG258, SG259 SG260, SG261, SG262, SG263, SG264 SG265, SG266, SG267 SG268, SG269, SG270, SG271, SG272 SG273, SG274, SG275 SG276, SG277, SG278, SG279, SG280 SG281, SG282, SG283 SG284, SG285, SG286, SG287, SG288 SG289, SG290, SG291 SG292, SG293, SG294, SG295, SG296 SG297, SG298, SG299 SG300, SG301, SG302, SG303, SG304 SG305, SG306, SG307 SG308, SG309, SG310, SG311, SG312 SG313, SG314, SG315 SG316, SG317, SG318, SG319, SG320 SG321, SG322, SG323 SG324, SG325, SG326, SG327, SG328 SG329, SG330, SG331 SG332, SG333, SG334, SG335, SG336, SG337, SG338, SG339, SG340, SG341, SG342, SG343, SG344, SG345, SG346, SG347, SG348, SG349, SG350, SG351, SG352, SG353, SG354, SG355, SG356, SG357, SG358, SG359, SG360, SG361, SG362, SG363, SG364, SG365, SG366, SG367, SG368, SG369, SG370, SG371, SG372, SG373, SG374, SG375, SG376, SG377, SG378, SG379, SG380, SG381, SG382, SG383, SG384, SG385, SG386, SG387, SG388, SG389, SG390, SG391, SG392, SG393, SG394, SG395, SG396, SG397, SG398, SG399, SG400, SG401, SG402, SG403, SG404, SG405, SG406, . SG407, SG408, SG409, SG410, SG411, SG412, SG413, SG414, SG415, SG416, SG417, SG418, SG419, SG420, SG421, SG422, SG423, SG424, SG425, SG426, SG427, SG428, SG429, SG430, SG431, SG432, SG433, SG434, SG435, SG436, SG437, SG438, SG439, SG440, SG441, SG442, SG443, SG444, SG445, SG446, SG447, SG448, SG449, SG450, SG451, SG452, SG453, SG454, SG455, SG456, SG457, SG458, SG459, SG460, SG461, SG462, SG465, SG468, SG469, SG470, SG471, SG472, SG473, SG474, SG475, SG476, SG477, SG478, SG479, SG480, SG481, SG482, SG483, SG484, SG485, SG486, SG487, SG488, SG489, SG490, SG491, SG492, SG493, SG494, SG495, SG496, SG497, SG498, SG499, SG500, SG501, SG502, SG503, SG504, SG505, SG506, SG507, SG508, SG509, SG510, SG511, SG512, SG513, SG514, SG515, SG516, SG517, SG518, SG519, SG520, SG521, SG522, SG523, SG524, SG525, SG526, SG527, SG428, SG529, SG530, SG531, SG532, SG533, SG534, SG535, SG536, SG537, SG538, SG539, SG540, SG541, SG542, SG543, SG544, SG545, SG546, SG547, SG548, SG549, SG550, SG551, SG552, SG553, SG554, SG555, SG556, SG557, SG558, SG559, SG560, SG561, SG562, SG563, or combinations thereof. Said oligonucleotides have been designed in such a way that, in addition to being specific for the corresponding genes whose expression is to be evaluated, they have a similar behavior, since they are of similar lengths and they all present GC contents comprised in the range of 40% to 60% , in addition to corresponding to zones located at less than 3,000 nucleotides of the 3 'end (poly (A)) of the mRNA to be detected and evaluated and of being constituted by sequences that coincide in their sense with that of the corresponding mRNA. Therefore, they are suitable for being used in the same test or forming part of a composition comprising combinations thereof. A particular embodiment of the invention consists of compositions comprising mixtures of several of said oligonucleotides. Especially preferred are those compositions comprising mixtures of oligonucleotides corresponding to significant genes for classifying a sample as associated with a certain neoplasm and / or for determining the future evolution thereof. Also especially preferred embodiments of the invention are those compositions comprising all of the oligonucleotides of the group consisting of: SG1, SG2, SG3, SG4, SG5, SG6, SG7, SG8, SG9, SG10, SG11, SG12, SG13, SG14, SG15, SG16, SG17, SG18, SG19, SG20, SG21, SG22, SG23, SG24, SG25, SG26 , SG27, SG28, SG29, SG30, SG31, SG32, SG33, SG34, SG35, SG36, SG37, SG38, SG39, SG40, SG41, SG42, SG43, SG44, SG45, SG46, SG47, SG48, SG49, SG50, SG51 , SG52, SG53, SG54, SG55, SG56, SG57, SG58, SG59, SG60, SG61, SG62, SG63, SG64, SG65, SG66, SG67, SG68, SG69, SG70, SG71, SG72, SG73, SG74, SG75, SG76 , SG77, SG78, SG79, SG80, SG81, SG82, SG83, SG84, SG85, SG86, SG87, SG88, SG89, SG90, SG91, SG92, SG93, SG94, SG95, SG96, SG97, SG98, SG99, SG100, SG101 , SG102, SG103, SG104, SG105, SG106, SG107, SG108, SG109, SG110, SG111, SG112, SG113, SG114, SG115, SG116, SG117, SG118, SG119, SG120, SG121, SG122, SG123, SG124, SG125, SG126 , SG127, SG128, SG129, SG130, SG131, SG132, SG133, SG134, SG134, SG136, SG136, SG137, SG138, SG139, SG140, SG141, SG142, SG143, SG144, SG145, SG146, SG147, SG148, SG149, SG150, SG151 , SG152, SG153, SG154, SG155, SG156, SG157, SG1 58, SG159, SG160, SG161, SG162, SG163, SG164, SG165, SG166, SG167, SG168, SG169 SG170, SG171, SG172, SG173 SG174, SG175, SG176, SG177 SG178, SG179, SG180, SG181 SG182, SG183, SG184, SG185 SG186, SG187, SG188, SG189 SG190, SG191, SG192, SG193 SG194, SG195, SG196, SG197 SG198, SG199, SG200, SG201 SG202, SG203, SG204, SG205 SG206, SG207, SG208, SG209 SG210, SG211, SG212, SG213 SG214, SG215, SG216, SG217 SG218, SG219, SG220, SG221 SG222, SG223, SG224, SG225 SG226, SG227, SG228, SG229 SG230, SG231, SG232, SG233 SG234, SG235, SG236, SG237 SG238, SG239, SG240, SG241 SG242, SG243, SG244, SG245 SG246, SG247, SG248, SG249 SG250, SG251, SG252, SG253 SG254, SG255, SG256, SG257 SG258, SG259, SG260, SG261 SG262, SG263, SG264, SG265 SG266, SG267, SG268, SG269 SG270, SG271, SG272, SG273 SG274, SG275, SG276, SG277 SG278, SG279, SG280, SG281 SG282, SG283, SG284, SG285 SG286, SG287, SG288, SG289 SG290, SG291, SG292, SG293 SG294, SG295, SG296, SG297 SG298, SG299, SG300, SG301 SG302, SG303, SG304, SG305 SG306, SG307, SG308, SG309 SG310, SG311, SG312, SG313 SG314, SG315, SG316, SG317 SG318, SG319, SG320, SG321 SG322, SG323, SG324, SG325 SG326, SG327, SG328, SG329 SG330, SG331, SG332, SG333 SG334, SG335, SG336, SG337 SG338, SG339, SG340, SG341 SG342, SG343, SG344, SG345 SG346, SG347, SG348, SG349 SG350, SG351, SG352, SG353, SG354, SG355, SG356, SG357, SG358 SG359, SG360, SG361, SG362, SG363, SG364, SG365, SG366 SG367, SG368, SG369, SG370, SG371, SG372, SG373, SG374 SG375, SG376, SG377, SG378, SG379, SG380, SG381, SG382 SG383, SG384, SG385, SG386, SG387, SG388, SG389, SG390 SG391, SG392, SG393, SG394, SG395, SG396, SG397, SG398 SG399, SG400, SG401, SG402, SG403, SG404, SG405, SG406 SG407, SG408, SG409, SG410, SG411 , SG412, SG413, SG414 SG415, SG416, SG417, SG418, SG419, SG420, sg421, SG422 SG423, SG424, SG425, SG426, SG427, SG428, SG429, SG430 SG431, SG432, SG433, SG434, SG435, SG436, SG437, SG438 SG439, SG440, SG441, SG442, SG443, SG444, SG445, SG446 SG447, SG448, SG449, SG450, SG451, SG452, SG453, SG454 SG455, SG456, SG457, SG458, SG459, SG460, SG461, SG462 SG465, SG468, SG470, SG472, SG473, SG474, SG475, SG476 SG477, SG478, SG479, SG480, SG481, SG482, SG483, SG484 SG485, SG486, SG487, SG488, SG489, SG490, SG491, SG492 SG493, SG494, SG495, SG496, SG497, SG498, SG499, SG500 SG501, SG502, SG503, SG504, SG505, SG506, SG507, SG508 SG509, SG510, SG511, SG512, SG513, SG514, SG515, SG516 SG517, SG518, SG519, SG520, SG521, SG522, SG523, SG524 SG525, SG526, SG527, SG428, SG529, SG530, SG531, SG532 SG533, SG534, SG535, SG536, SG537 , SG538, SG539, SG540, SG541, SG542, SG543, SG544, SG545, SG546, SG547, SG548, SG549, SG550, SG552, SG553, SG554, SG555, SG556, SG557, SG558, SG559, SG560, SG561, SG562 , SG563. Additionally, the invention provides useful oligonucleotides to be used as controls in the method of the invention. On the one hand, as integrity controls, the pairs of oligonucleotides SG463 and SG464 (complementary, respectively, to the 5 'and 3' ends of the β-actin gene) and SG466 and SG467 (complementary, respectively, to the termini) are provided. 'and 3' of the GAPD gene). Additionally, oligonucleotides SSPC1, SSPC2, SSPC3, SSPC4, SSPC5, SSPC6 and SSPC7 are provided, which can be used as exogenous internal positive controls of the quality of the process after being added to the sample containing the starting mRNA polyadenylated nucleic acid molecules containing fragments corresponding in sequence with these oligonucleotides (such as the transcripts corresponding to the genes from which said oligonucleotides are derived) and which are subjected to the same processing as the starting mRNA, as well as the SCN2 oligonucleotides, SCN3, SCN6, SCN8, SCN11, SCN12 and SCN13, designed to be used as positive hybridization controls, and the oligonucleotides SCN1, SCN5, SCN7, SCN10, SCI, SC2, SC3, SC4, SC5, SC6 and SC7, designed to be used as negative controls; all of them meet the conditions of presenting low homology with human genes, in addition to fulfilling the same conditions of the oligonucleotides complementary to human genes of being of similar lengths and presenting all of them GC contents comprised in the range of 40% to 60%, correspond to zones located at less than 3,000 nucleotides of the 3 'end (poly (A)) of the non-human mRNA that would be able to detect and be made up of sequences that coincide in their sense with that of the corresponding mRNA. Any composition containing at least one of the oligonucleotides SG463, SG464, SG466, SG467, SSPC1, SSPC2, SSPC3, SSPC4, SSPC5, SSPC6, SSPC7, SCN2, SCN3, SCN6, SCN8, SCN11, SCN12, SCN13, SCN1, SCN5 , SCN7, SCN10, SCI, SC2, SC3, SC4, SC5, SC6 and SC7, in combination with at least one of the oligonucleotides complementary to human genes of the invention mentioned above is also a composition included within the scope of the present invention. It is especially preferred that the oligonucleotides that are part of a composition of the invention be attached to a solid support. In particular, those of such compositions are preferred in which the distribution of the oligonucleotides on the solid support is ordered, the solid support used is a rectangular glass similar to the microscope slides and the oligonucleotides are attached to the glass by means of covalent bonds; the compositions that meet such characteristics are referred to in the remainder of the specification with the words "microarray", "chip" or "microchip". Among these compositions in the form of a microarray, there is a special preference for those that contain more than one copy of each of the oligonucleotides that are part of it, it being very particularly preferred that the number of copies of each of the oligonucleotides present be from at least 12. Any diagnostic device comprising a composition of the invention is also included within the scope of the invention. The term "diagnostic device" refers not only to those that serve to determine whether an individual suffers from a disease, but those that serve to classify the disease that an individual suffers as belonging to a subtype associated with a certain form of evolution in the future of said disease and, therefore, also have prognostic value of the future evolution of the disease. The invention also provides a method for diagnosing a neoplasm originating from hematopoietic cells and / or predicting the evolution thereof comprising in vi tro detection from a biological sample and the statistical analysis of the expression level of at least one significant gene to classify the sample as associated or not to said neoplasia, gene that is selected from the group consisting of GABARAP, NPM3, ABCB1, ABCB4, ABCC3, ABCC5, ABCC6, ABHD1, ABL1, ACTN1, AFlq, AKR1A1, ALDH1A1, ALK, ANK2, A PEP, ANXA6, ANXA7, APAF1, APEX, ARHGEF2, ARS2, ASNS, ATIC, ATM, ATP50, BAX, BCL10, BCL2, BCL2A1, BCL2L1, BCL2LAA, BCL3, BCL6, BCL7A, BCL7b, BCR, BECN1, BIK , BIRC3, BIRC5, BLMH, BLR1, BLVRB, BMI1, BMP6, BRMS1, BST2, BTG1, BUB1, C21orf33, C5orfl3, CA12, CALD1, CANP2, CASC3, CASP1, CASP3, CASP4, CASP5, CASP6, CASP7, CASP8, CASP9 , CAST, CATSD, CBFA2T1, CBFB, CCNA1, CCNB1, CCND1, CCND2, CCND3, CCNE1, CCR6, CCR7, CCT6A, CD14, CD19, CD 2, CD22, CD24, CD28, CD33, CD34, CD34, CD38, CD38, CD4E, CD4, CD44, CD47, CD48, CD48, CD58, CD58, CD74, CD74, CD79, CD79, CD8, CD8, CD8, CD8, CDA, CDC25A, CDC25B, CDK2, CDK4, CDK5R1, CDKN1A, CDKN1B, CDKN1C, CDKN2A, CDK2B, CDKN2C, CDKN3, CDW52, CEBPA, CEBPB, CEBPD, CFL1, CK T1, CKS2, CML66, C0L3A1, COL4A6, CR2, CREB1, CREBBP, CRYAB, CSF2, CSF3, CSRP2, CTGF, CTSB, CUZD1, CXADR, CXCL9, CXCR3, CXCR4, CYC1, CYP1A1, CYP2A6, DAD-1, DAPK1, DCK, DDX6, DEK, DHFR, DLAD, DNAJA1, D MT3B, DNTT, D0K1, DPF2, DPP4, DRG1, DRP2, E2F1, EB-1, EBI2, EDF1, EEF1A1, EEF1B2, EEF1D, EEF1G, EFNB1, EGFR, EGR1, EIF2B2, EIF3S2, EIF4B, EIF4E, EIF5A, ELF1 , ELF4, ENPP1, EphA3, EPOR, ERBB2, ERBB4, ERCC1, ERCC2, ERCC3, ERCC5, ERCC6, ETS1, ETS2, ETV6, ETV7, EZH2, FABP5, FADD, FAIM3, FAM38A, FARP1, FAT, FCER2, FCGR3A, FCGR3B , FGFR1, FGFR3, FGR, FHIT, FKBP9, FLI1, FLJ22169, FLT3, FN1, FNTB, FOS, FUS, G1P2, GABPB2, GATA1, GATA2, GATA3, GCET2, GDI2, GGA3, GJA1, G LUD1, GNL3, GOT1, GRB2, GRIA3, GRK4, GSTP1, GSTT1, GUSB, GZMA, H2AFX, H3F3A, HCK, HELLS, HIF1A, HIST1H2BN, HLA-A, HLA-DPAI, HLA-DQAl, HLA-DRA, HLA- DRB3, HLF, HMMR, HNRPH3, HNRPL, HOXA10, HOXA9, HOXD8, HOXD9, HRAS, HSD17B1, HSPB1, IBSP, ICAM1, ICAM3, ID2, IER3, IFRD1, IGFBP2, IGFBP3, IGFIR, IGLV6-57, IL10, IL15, IL1B, IL2, IL2RA, IL3, IL32, IL3RA, IL4R, IL6R, IL6R, IL8, ILF2, IRF1, IRF2, IRF4, IRF8, ITGA2, ITGA3, ITGA4, ITGA5, ITGA6, ITGAL, ITGAM, I GAX, ITGB1, ITGB2 , JAK1, JAK2, JUNB, KAI1, KIAA0247, KIAA0864, KIT, KLF1, KLF13, KRAS2, KRT18, LADH, LAG3, LASP1, LCK, LCP1, LEPR, LGALS3, LGALS7, LIF, LIMS1, LM02, LOC285148, LRP, LSP1 , LYL1, LYN, LYZ, MAFB, MAFK, MAGEA1, MAL, MAP3K12, MAP4K1, MAPK10, MAZ, MBP1, MCL1, MCM3, C7, MD2, MEIS1, MEN1, MERTK, MKI67, MLF1, MLF2, MLL, MLLTIO , MME, MMP2, MMP7, MMP8, MMP9, MNDA, MPL, MPO, MRPL37, MS4A1, MTCP1, UC-1, X1, MYB, MYBL1, MYC, MYOD1, NCALD, NCAM1, NCL, NDP52, NDRG1, NDUFA1, NDUFB, NF1, NFATC1, NFIC, NFKB1, NFKB1A, NINJ1, NPM1, NR3C1, UMA1, NXF1, ODC1, OGGI, OLIG2, OPRD1, pl4ARF, P55CDC, PABPC1, PAX5, PAX6, PAX8, PBX1, PBX3, PCA1 , PCD, PCNA, PDCD1, PDGFRB, PDGFRB, PDHA1, PGF, PGRMC1, PICALM, PLA2G6, PLAU, PLK1, PLP, PLS3, PLZF, PL, PM 1, POLR2C, POU2F2, PPP1CC, PRAME, PRKCI, PRKCQ, PRKDC, PRL, PRTN3, PSMA5, PSMB4, PS C5, PSMD7, PTEN, PTGS1, PTHLH, PTK2, PTK2B, PTN, PTPRCCD, PYGB, RAD51, RAF1, RAG1, RARE, RARB, RB1, RBBP4, RBBP6, RBBP8, RBP4, RET , RGS1, RGS1, RIS1, RORA, RPL17, RPL23A, RPL24, RPL36A, RPL37A, RPL41, RPS3, RPS5, RPS9, RUNX1, RXRA, S100A2, S100A8, SDC1, SDHD, SELE, SELL, SEP 1, SERPINA9, SERPINB5, SERPNINA9, SFTPB, SIAT4A, SLC7A5, SNRPB, SOSTDC1, SP1, SPI1, SPN, SPRR1A, SREBF1, SSBP1, STAT1, STAT3, STAT5B, SUMO1, TACSTD2, TAGLN2, TAL1, TBP, TCEB1, TCF1, TCF3, TCF7, TCL1A, TCRbeta, TEGT, TERF1, TERT, TFCP2, TFRC, THBS1, THPO, TIA-2, TIAM1, TK1, TLX1, TMEM4, TNF, TNFRSFIOC, TNFR SF1A, TNFRSF25, TNFRSF5, TNFRSF6, TNFRSF8, TNFSF10, TNFSF5, TNFSF6, TOP2A, TOPORS, TP73, TRA @, TRADD, TRAF3, TRAP1, TRIB2, TXNRD1, UBE2C, UHRF1, UVRAG, VCA 1, VEGF, VPREB1, WBSCR20C, WNT16, T1, XBP1, XP06, XRCC3, XRCC5, ??? 70, ZFPL1, ZNF42, ZNFN1A1, ???, 18S rRNA, 28S rRNA, and whose level of expression is determined by evaluating the concentration of its corresponding mRNA by using at least one probe having a sequence complementary to a fragment of a strand of said gene, which probe is selected from the group of oligonucleotides composed of: SG1, SG2, SG3, SG4, SG5, SG6, SG7, SG8, SG9, SG10, SG11, SG12, SG13, SG14, SG15, SG16, SG17, SG18, SG19, SG20, SG21, SG22, SG23, SG24, SG25, SG26, SG27, SG28, SG29, SG30, SG31, SG32, SG33, SG34, SG35, SG36, SG37, SG38, SG39, SG40, SG41, SG42, SG43, SG44, SG45, SG46, SG47, SG48, SG49, SG50, SG51, SG52, SG53, SG54, SG55, SG56, SG57, SG58, SG59, SG60, SG61, SG62, SG63, SG64, SG65, SG66, SG67, SG68, SG 69, SG70, SG71, SG72, SG73, SG74, SG75, SG76, SG77, SG78, SG79, SG80, SG81, SG82, SG83, SG84, SG85, SG86, SG87, SG88, SG89, SG90, SG91, SG92, SG93, SG94, SG95, SG96, SG97, SG98, SG99, SG100, SG101, SG102, SG103, SG104, SG105, SG106, SG107, SG108, SG109, SG110, SG111, SG112, SG113, SG114, SG115, SG116, SG117, SG118, SG119, SG120, SG121, SG122, SG123, SG124, SG125, SG126, SG127, SG128, SG129, SG130, SG131, SG132, SG133, SG134, SG135, SG136, SG137, SG138 ,. SG139, SG140, SG141, SG142, SG143, SG144, SG145, SG146, SG147, SG148, SG149, SG150, SG151, SG152, SG153, SG154, SG155, SG156, SG157, SG158, SG15 9, SG160, SG161, SG162, SG163, SG164, SG165, SG166, SG16 7, SG168, SG169, SG170, SG171, SG172, SG173, SG174, SG17 5, SG176, SG177, SG178, SG179, SG180, SG181, SG182, SG18 3, SG184, SG185, SG186, SG187, SG188, SG189, SG190, SG19 1, SG192, SG193, SG194, SG195, SG196, SG197, SG198, SG19 9, SG200, SG201 SG202, SG203, SG204, SG205, SG206, SG20 7, SG208, SG209, SG210, SG211, SG212, SG213, SG214, SG21 5, SG216, SG217, SG218, SG219, SG220, SG221, SG222, SG22 3, SG224, SG225, SG226, SG227, SG228, SG229, SG230, SG23 1, SG232, SG233, SG234, SG235, SG236, SG237, SG238, SG23 9, SG240, SG241, SG242, SG243, SG244, SG245, SG246, SG24 7, SG248, SG249 SG250, SG251, SG252, SG253, SG254, SG255, SG256, SG257, SG258, SG259, SG260, SG261, SG262, SG263, SG264, SG265, SG266, SG267, SG268, SG269, SG270, SG27 1, SG272, SG273, SG274, SG275, SG276, SG277, SG278, SG27 9, SG280, SG281, SG282, SG283, SG284, SG285, SG286, SG28 7, SG288, SG289, SG290, SG291, SG292, SG293, SG294, SG29 5, SG296, SG297, SG298, SG299, SG300, SG301, SG302, SG30 3, SG304, SG305 SG306, SG307, SG308, SG309, SG310, SG31 1, SG312, SG313, SG314, SG315, SG316, SG317, SG318, SG31 9, SG320, SG321, SG322, SG323, SG324, SG325, SG326, SG32 7, SG328, SG329, SG330, SG331, SG332, SG333, SG334, SG335, SG336, SG337, SG338, SG339, SG340, SG341, SG342, SG343, SG344, SG345, SG346 SG347, SG348, SG349, SG350, SG351, SG352, SG353, SG354 SG355, SG356, SG357, SG358, SG359, SG360, SG361, SG362 SG363, SG364, SG365, SG366, SG367, SG368, SG369, SG370 SG371, SG372, SG373, SG374, SG375, SG376, SG377, SG378 SG379, SG380, SG381, SG382, SG383, SG384, SG385, SG386 SG387, SG388, SG389, SG390, SG391, SG392, SG393, SG394 SG395, SG396, SG397, SG398, SG399, SG400, SG401, SG402 SG403, SG404, SG405, SG406, SG407, SG408, SG409, SG410 SG411, SG412, SG413, SG414, SG415, SG416, SG417, SG 18 SG419, SG420, SG421, SG422, SG423, SG424, SG425, SG426 SG427, SG428, SG429, SG430, SG431, SG432, SG433, SG434 SG435, SG436, SG437, SG438, SG439, SG440, SG441, SG442 SG443, SG444, SG445, SG446, SG447, SG448, SG449, SG450 SG451, SG452, SG453, SG454, SG455, SG456, SG457, SG458 SG459, SG460, SG461, SG462, SG465, SG468, SG469, SG470 SG471, SG472, SG473, SG474, SG475, SG476, SG477, SG478 SG479, SG480, SG481, SG482, SG483, SG484, SG485, SG486 SG487, SG488, SG489, SG490, SG491, SG492, SG493, SG494 SG495, SG496, SG497, SG498, SG499, SG500, SG501, SG502 SG503, SG504, SG505, SG506, SG507, SG508, SG509, SG510 SG511, SG512, SG513, SG514, SG515, SG516, SG517, SG518 SG519, SG520, SG521, SG522, SG523, SG524, SG525, SG526, SG527, SG428, SG529, SG530, SG531, SG532, SG533, SG534, SG535, SG536, SG537, SG538, SG539, SG540, SG541, SG542, SG543, SG544, SG545, SG546, SG547, SG548, SG549, SG550, SG551, SG552, SG553, SG554, SG555, SG556, SG557, SG558, SG559, SG560, SG561, SG562, SG563. The genes that are part of the aforementioned group are human genes. Therefore, whenever they are used in the future, the words "subject" or "individual" will refer to a human being. A particular case of this method is that which comprises an additional preliminary stage of identification of significant genes for the classification of the biological sample analyzed as associated or not associated with a specific type of neoplasia originated from hematopoietic cells, classification in which it includes not only the diagnosis of the existence of said neoplasm in the individual from which the sample has been extracted, but it may also consist, in an additional or alternative way, in the discrimination between specific subtypes of said neoplasm that correspond to future forms of evolution of said neoplasm, thus constituting the classification in one or the other subtype a prognosis of the evolution of the neoplasm considered in the future. In this particular case of the method of the invention comprising a previous step of identifying significant genes to perform the desired classification, said previous stage comprises the sub-steps of: a) deciding the possible categories in which the sample can be classified; b) Obtain biological samples from individuals that have previously been assigned by a method different from the one claimed to any of the possible classification categories, so that samples of each of the possible categories are available; c) obtain the total mRNA from each of the samples; d) obtain the corresponding total cRNA, marked by a method that allows its subsequent detection, of at least one aliquot of each of the mRNA samples, aliquot to which is added before obtaining the cRNA at least one sequence of polyadenylated nucleotides of low homology with human genes to act as an internal positive control of the process; e) adding to each of the cRNA aliquots to be used in step f) at least one oligonucleotide of low homology with human genes other than and not complementary to any possible nucleotide sequence that has been added in step d ), to act as a positive hybridization control; f) hybridizing, under stringent conditions, at least one aliquot of total cRNA of each of the samples with at least one microarray comprising at least two copies of each of the oligonucleotides of the group consisting of: SG1, SG2, SG3, SG4 , SG5, SG6, SG7, SG8, SG9, SG10, SG11, SG12, SG13, SG14, SG15, SG16, SG17, SG18, SG19, SG20, SG21, SG22, SG23, SG24, SG25, SG26, SG27, SG28, SG29 , SG30, SG31, SG32, SG33, SG34, SG35, SG36, SG37, SG38, SG39, SG40, SG41, SG42, SG43, SG44, SG45, SG46, SG47, SG48, SG49, SG50, SG51, SG52, SG53, SG54 , SG55, SG56, SG57, SG58, SG59, SG60, SG61, SG62, SG63, SG64, SG65, SG66, SG67, SG68, SG69, SG70, SG71, SG72, SG73, SG74, SG75, SG76, SG77, SG78, SG79 , SG80, SG81, SG82, SG83, SG84, SG85, SG86, SG87, SG88, SG89, SG90, SG91, SG92, SG93, SG94, SG95, SG96, SG97, SG98, SG99, SG100, SG101, SG102, SG103, SG104 , SG105, SG106, SG107, SG108, SG109, SG110, SG111, SG112, SG113, SG114, SG115, SG116, SG117, SG118, SG119, SG120, SG121, SG122, SG123, SG124, SG125 , SG126, SG127, SG128, SG129, SG130, SG131, SG132, SG133, SG134, SG134, SG136, SG136, SG137, SG138, SG139, SG140, SG141, SG142, SG143, SG144, SG145, SG146, SG147, SG148, SG149, SG150 , SG151, SG152, SG153, SG154, SG155, SG156, SG157, SG158, SG159, SG160, SG161, SG162, SG163, SG164, SG165, SG166, SG16 7, SG168, SG16 9, SG170, SG171, SG172, SG173, SG174, SG17 5, SG176, SG17 7, SG178, SG179, SG180, SG181, SG182, SG18 3, SG184, SG18 5, SG186, SG187, SG188, SG189, SG190, SG19 1, SG192, SG19 3, SG194, SG195, SG196, SG197, SG198, SG19 9, SG200, SG20 1, SG202, SG203, SG204, SG205, SG206, SG20 7, SG208, SG20 9, SG210, SG211, SG212, SG213, SG214, SG21 5, SG216, SG21 7, SG218, SG219, SG220, SG221, SG222, SG22 3, SG224, SG22 5, SG226, SG227, SG228, SG229, SG230, SG23 1, SG232, SG23 3, SG234, SG235, SG236, SG237, SG238, SG23 9, SG240, SG24 1, SG242, SG243, SG244, SG245, SG246, SG24 7, SG248, SG24 9, SG250, SG251, SG252, SG253, SG254, SG25 5, SG256, SG25 7, SG258, SG259, SG260, SG261, SG262, SG26 3, SG264, SG26 5, SG266, SG267, SG268, SG269, SG270, SG27 1, SG272, SG27 3, SG274, SG275, SG276, SG277, SG278, SG27 9, SG280, SG28 1, SG282, SG283, SG284, SG285, SG286, SG28 7, SG288, SG28 9, SG290, SG291, SG292, SG293, SG294, SG29 5, SG296, SG29 7, SG298, SG299, SG300, SG301, SG302, SG30 3, SG304, SG30 5, SG306, SG307, SG308, SG309, SG310, SG31 1, SG312, SG31 3, SG314, SG315, SG316, SG317, SG318, SG31 9, SG320, SG32 1, SG322, SG323, SG324, SG325, SG326, SG32 7, SG328, SG32 9, SG330, SG331, SG332, SG333, SG334, SG33 5, SG336, SG33 7, SG338, SG339, SG340, SG341, SG342, SG343, SG344, SG345 SG346, SG347, SG348, SG349, SG350, SG35 1, SG352, SG353 SG354, SG355, SG356, SG357, SG358, SG35 9, SG360, SG361 SG362, SG363, SG364, SG365, SG366, SG36 7, SG368, SG369 SG370, SG371, SG372, SG373, SG374, SG37 5, SG376, SG377 SG378, SG379, SG380, SG381, SG382, SG38 3, SG384, SG385 SG386, SG387, SG388, SG389, SG390, SG39 1, SG392, SG393 SG394, SG395, SG396, SG397, SG398, SG39 9, SG400, SG401 SG402, SG403, SG404, SG405, SG406, SG40 7, SG408, SG409 SG410, SG411, SG412, SG413, SG414, SG41 5, SG416, SG417 SG418, SG419, SG420, SG421, SG422, SG42 3, SG424, SG425 SG426, SG427, SG428, SG429, SG430, SG43 1, SG432, SG433 SG434, SG435, SG436, SG437, SG438, SG43 9, SG440, SG441 SG442, SG443, SG444, SG445, SG446, SG44 7, SG448, SG449 SG450, SG451, SG452, SG453, SG454, SG45 5, SG456, SG457 SG458, SG459, SG460, SG461, SG462, SG46 5, SG468, SG469 SG470, SG471, SG472, SG473, SG474, SG47 5, SG476, SG477 SG478, SG479, SG480, SG481, SG482, SG48 3, SG484, SG485 SG486, SG487, SG488, SG489, SG490, SG49 1, SG492, SG493 SG494, SG495, SG496, SG497, SG498, SG49 9, SG500, SG501 SG502, SG503, SG504, SG505, SG506, SG50 7, SG508, SG509 SG510, SG511, SG512, SG513, SG514, SG51 5, SG516, SG517 SG518, SG519, SG520, SG521, SG522, SG52 3, SG524, SG525 SG526, SG527, SG428, SG529, SG530, SG531, SG532, SG533, SG534, SG535, SG536, SG537, SG538, SG539, SG540, SG541, SG542, SG543, SG544, SG545, SG546, SG547, SG548, SG549, SG550, SG551, SG552, SG553, SG554, SG555, SG556, SG557, SG558, SG559, SG560, SG561, SG562, SG563, microarray which additionally comprises: a. at least two points corresponding to aliquots of the solvent in which the oligonucleotides were at the moment of their deposition on the surface of the microarray, so that they serve as targets, b. at least two copies of at least one oligonucleotide for each of the polyadenylated sequences added in step d), oligonucleotide whose sequence will correspond to a fragment, other than the polyadenylation zone, of the polyadenylated nucleotide sequence whose evolution in the process has to control; c. for each of the oligonucleotides added in step e), at least two copies of an oligonucleotide complementary thereto; d. at least two copies of each member of at least one pair of oligonucleotides in which the sequence of one of the members corresponds to a sequence of the 5 'region and the sequence of the other corresponds to a sequence of the 3' region of the mRNA of a gene that is expressed constitutively in any cell of nematopoietic origin; and. at least two copies of at least one oligonucleotide of low homology with human genes distinct from any of the oligonucleotides defined in section b. and distinct from any of the synthetic oligonucleotides optionally added in step e); g) detect and quantify the cRNA signal hybridized with each of the copies of each of the oligonucleotides present in the microarray, as well as the signal corresponding to the solvent points; h) calculating the average level of hybridization intensity of each of the oligonucleotides of the microarray by calculating the average of the intensities of the copies of each of the oligonucleotides; i) give hybridization as valid if the following conditions are met: a. the ratio between the mean intensity and the average background of all the oligonucleotides of the microarray is greater than 10; b. the value of the mean variation coefficient of all oligonucleotide replicates must be less than 0.3; c. the mean value of negative control must be less than 2.5 times the average value of the points corresponding to the solvent; d. there is a signal both in the hybridization controls and in the internal positive controls used as process control; j) normalize the data; k) eliminate the oligonucleotides with values of mean intensity less background noise less than about 2 times the average value obtained with the points corresponding to the solvent, as well as the oligonucleotides with an interquartile range of normalized intensity throughout the samples smaller than 0.3; 1) perform the statistical analysis to find the statistically significant oligonucleotides to differentiate between the different categories and be able to perform the classification of a sample that has not been previously assigned to any category, choosing said oligonucleotides from those that have not been eliminated in the previous steps , until obtaining the "n" oligonucleotides that either have a p value lower than a limit that is chosen from the open range of 0 to 0.05, preferably using a method with the capacity to reduce false positives, or those that best define the established categories; m) verify that the grouping of the samples according to the differences in the intensities between the different samples detected for the statistically significant oligonucleotides results in the samples being classified in the same categories to which they had previously been assigned by a different method. It is preferred that the average value calculated in section h) be a bounded average, for which it is preferable that the microarray comprises at least four copies of each of the oligonucleotides present therein. Normalization can be done with different methods. Preference is given to the use of functions contained in free access packages over the Internet designed for the manipulation, calculation and graphical representation of data, such as the packages designed for the R programming language, available for download from CRAN (http : // were. r-proj ect. org /) or Bioconductor (http: //www.bioconductor .org). The "variance stabilization normalization" method available in the "vsn" package of R is especially preferred. The identification of statistically significant oligonucleotides to differentiate between the different categories can be done by different methods, preference being given to those in which a value is determined. p that determines the threshold of probability below which all the genes whose difference of expression has a value lower than p would be considered significant and, among these, those that have the capacity to make corrections on the value of p, as they can be, among others , the Bonferroni method or the Welch test. The value of p will be chosen from the open interval from 0 to 0.05, with preference being given, where possible, to p values close to 0.001 and with correction, the maximum value of which may be increased to 0.05 (a value that is not included among the possible) until statistically significant oligonucleotides are found to differentiate between the categories between which the samples are to be classified. One possibility to perform these calculations is, again, the use of functions contained in free access packages over the Internet designed for the manipulation, calculation and graphical representation of data. Specifically, for the identification of statistically significant oligonucleotides, the mt.maxT function of the multtest package of R can be used. Another possibility for the identification of statistically significant oligonucleotides to be able to differentiate between the categories of established samples is the use of the method of "nearest" shrunken centroids ", a variation of the "nearest centroids" method (Tibshirani et al., 2002), which identifies a group of genes that best characterizes a predefined class and uses this group of genes to predict the class to which new samples belong. For this, it is possible to resort again to functions contained in freely accessible packages on the Internet, such as the "pam" package of R, in which functions can be found to carry out the so-called "Prediction Analysis for Microarrays (PAM)", which makes use of the method of "nearest shrunken centroids".

Once the statistically significant genes have been identified to distinguish between categories of samples established from the corresponding oligonucleotides, they can be used to classify new samples by similarity between the expression profile of those genes in the analyzed sample and that corresponding to each of the categories of classification. Alternatively, when the possible classification categories are only 2 (which will be normal when you want to diagnose the presence or absence of a certain type of leukemia in an individual), a mathematical function of classification of samples can be obtained that determines the value of the probability "pi" that a sample "i" belongs to one or another category. For this, a subset of the samples that have been previously assigned to each of the possible categories is chosen by a method different from that of the invention and the value 0 is arbitrarily associated with the probability corresponding to each of the samples of one of the the categories "a" (usually, the category of "not associated with the leukemia that is to be diagnosed") of belonging to the other possible category, while each of the samples of the subgroup pertaining to the other possible category "b" ( usually, the category of "associated with the leukemia that is to be diagnosed" receives arbitrarily the value "1" for its probability of belonging to its own category. With this, logistic regression is used to calculate, with the help of the probability values assigned to each of the samples and the normalized bounded average intensity values obtained for each of the samples with each of the "n" oligonucleotides that have been identified as a statistically significant oligonucleotide in the previous stage, coefficients for each of said oligonucleotides that allow obtaining a probability function pi that a sample "i" belongs to the category "b", a function that will be of the type pi = l / (l + e-xi) and that results from the algebraic transformation of the expression that equals the Neperian logarithm of the quotient between the probability that an event occurs and the probability that it does not occur to a function xi that includes as variables each the factors that can influence the event, ie function xi that, in the present case, will depend on the intensity values obtained for each of the statistically significant oligonucleotides and that responds to an expression of the type: n Xi = constant +? (coef_oligm * Imni_oligm) m = l where coef_oligm represents the calculated coefficient for a particular oligonucleotide "m" Imni_oligm represents the mean value of normalized intensity obtained in the hybridization of the sample i calculated for the oligonucleotide "m" "m" varies from 1 to "n" n is the number total of oligonucleotides considered significant The p function obtained after calculating by logistic regression the coefficient corresponding to each oligonucleotide allows to classify a sample "i" as belonging to one or another category, considering that the values of pi greater than 0.5 (and that will be less than or equal to 0) indicate that the sample belongs to the category "b", while the values of pi less than 0.5 indicate that the sample belongs to the category "a". This function p will be considered valid if, when applied to the samples from which it has been deducted, it is able to classify them correctly and, in addition, when applied to the subgroup of samples that had not been taken into account to deduce the function but whose category is known to have been previously assigned by a method different from that of the invention, is also able to classify them correctly. Alternatively, when the identification of statistically significant genes has been carried out using the "Prediction Analysis for Microarrays" method, the classifier can be obtained with the corresponding functions of the "pamra" package of R, which also starts from the assignment of the probability value 0 to a subgroup of members of one of the categories and the probability value 1 to a subgroup of members of the other category. Again, the calculation of coefficients for statistically significant oligonucleotides allows the calculation of probability values of belonging to one or another category, also considering that values higher than 0.5 indicate the membership of the category whose members were arbitrarily assigned the value 1 and values less than 0.5 indicate membership in the other category. A particular case of the method of the invention is one in which it is desired to classify samples as associated or not to some type of leukemia. In that case, blood samples, especially those from peripheral blood, are preferred as biological samples to carry out the method of the invention in vi tro. Once the statistically significant genes for associating a sample to a certain type of neoplasm originated from hematopoietic cells are identified, the method of the invention can be used to classify samples according to the level of expression of said genes in said samples. The neoplasm may be, for example, a specific type of leukemia. A particular case of this embodiment of the method of the invention is the association of a sample to chronic lymphatic leukemia, thus allowing the diagnosis of this disease by the method of the invention. For this, they are considered as significant genes whose level of expression is analyzed when applying the method of the invention at least those of the group of CD79A, FAI 3, HLA-DRA, HLA-DRB3, HLA-DQA1 and the analysis is performed on samples of blood. The method can be applied by additionally including analysis of the level of expression of at least the IRF8 and C0L3A1 genes. Preferably, the analysis of the level of expression of these genes is carried out by evaluating the level of their corresponding mRNAs by hybridization of their corresponding cRNA with the oligonucleotides SG117, SG428, SG459, SG507, SG508, SG461 and SG493, which are preferred to be associated to a solid support forming part of a microarray. When the evaluation of the cRNA hybridized with each of these oligonucleotides is carried out thanks to the previous labeling of the cRNA with biotin, the staining of the microarray hybridized with streptavidin conjugated with a fluorophore and the detection of the signal emitted by said fluorophore, it is preferred that the fluorophore is Cy3, which allows the diagnosis of the presence of CLL in the subject from whom the sample has been extracted by means of the classification of the sample "i" analyzed as associated to LLC from the calculation of the probability that said sample is associated to LLC from the formula pi = l / (l + e ~ Xl), where xi is calculated by the formula Xi = -719,241486 + (2.44756372 * Imni_CD79A) + (7.38657611 * Imni FAIM3 ) + (23,1465464 * Imni_HLA-DRA) + (43,6287742 * Imni_IRF8) - (19,3978182 * Imni_C0L3Al) - (2,80282646 * Imni_HLA-DRB3) + (49,5345672 * Imni_HLA-DQAl) formula in which each of the values called by the abbreviation "Imni" followed by the abbreviation of a gene refers to the average value of normalized intensity obtained after detecting the signal of hybridization corresponding to the oligonucleotide that is being used as a probe to evaluate the expression of said gene and that allows to classify the subject as a subject that does not suffer CLL if the value of pi is less than 0.5 and as the subject that has CLL if the value of p is greater than 0.5. Alternatively, significant genes whose level of expression is analyzed by applying the method of the invention for the diagnosis of CLL at least those of the group of CD79A, FAI 3, HLA-DRA, HLA-DRB3, HLA-DQA1, including additionally can be considered. the analysis of the expression level of at least the CD 52 gene. Preferably, the analysis of the level of expression of these genes is carried out by evaluating the level of their corresponding mRNA by hybridization of their corresponding cR A with the oligonucleotides SG117, SG428, SG459, SG507, SG508 and SG237, which is preferred to be associated with a solid support forming part of a microarray. Another particular case of the application of the method of the invention to classify samples as associated to a specific type of leukemia according to the level of expression in said samples of statistically significant genes constitutes the classification of a sample as associated to a specific subtype of lymphatic leukemia. chronic, the "stable" LLC or the "progressive" LLC, which allows the method of the invention to predict the future evolution of subjects who have been diagnosed with CLL. When the analyzed samples are from peripheral blood, the genes considered statistically significant for the classification of the samples are at least the genes PSMB4, FCER2 and POU2F2, and the level of expression of at least one gene selected from the group composed of ODC1 can be further analyzed. , CD79A, CD2, CD3E, CD5, MS4A1, EIF4E, FHIT, NR3C1, LCP1, APK10, ABCC5, XRCC3, CML66, PLZF, RBP4 or all of them. A further aspect of the invention is the use of devices for evaluating the level of expression of at least one of the genes of the group consisting of PSMB4, FCER2, POU2F2, ODC1, CD79A, CD2, CD3E, CD5, MS4A1, EIF4E, FHIT. , NR3C1, LCP1, MAPK10, ABCC5, XRCC3, CML66, PLZF, RBP, CD79A, FAIM3, HLA-DRA, HLA-DRB3, HLA-DQA1, IRF8 and COL3A1 for the purpose of diagnosing the presence of CLL in an individual and / or forecast its evolution. A particular case of this aspect of the invention is the use of devices for evaluating the level of expression of at least one gene of the group consisting of CD79A, FAI 3, HLA-DRA, HLA-DRB3, HLA-DQA1, IRF8 and C0L3A1 for the diagnosis of the presence of CLL in an individual, in which it is preferred that the device evaluates at least the level of expression of the genes CD79A, FAIM3, HLA-DRA, HLA-DRB3, HLA-DQA1, being able to evaluate the device, additionally, the level of expression of at least the IRF8 and C0L3A1 genes or of at least the CD 52 gene. Another particular case of this aspect of the invention is the use of devices for evaluating the expression level of at least one gene of the group composed of PSMB4, FCER2, POU2F2, ODC1, CD79A, CD2, CD3E, CD5, MS4A1, EIF4E, FHIT, NR3C1, LCP1, MAPK10, ABCC5, XRCC3, CML66, PLZF, RBP4, CD79A, FAI 3, HLA-DRA, HLA-DRB3, HLA-DQA1, IRF8 and COL3A1 to predict the future evolution of CLL in an individual. Detailed description of the invention; Design of the "microarray" device Genes included in the "microarray" A review of the scientific literature was made and genes were selected for their special involvement in the biology of blood cells or in the pathology of the different neoplasms. The selected genes can be included within these 4 major groups: a) With an important role in the biology of hematopoietic cells: Genes whose protein is expressed or repressed in the different stages through which these cells cross in their differentiation into mature forms. - Genes whose protein is expressed in a specific way depending on the lineage to which the cell belongs. - Genes that encode adhesion molecules b) Involved in different types of hematological neoplasms: - Genes whose expression (at the level of MRNA or protein) is altered in different types of neoplasms, or associated with resistance to chemotherapy c) Cancer-related: - Genes that encode proteins associated with proliferation, metastasis or genes whose expression is found increased in a large number of tumors. d) Described in publications related to neoplasms: Genes that, without having a special relationship with hematological neoplasms or with blood cell biology, have appeared in the scientific literature as statistically associated with a type of neoplasm. The characteristics of the genes can be found, for example, at: www. ncbi. nlm. nih gov / Genbank, selecting the "Gene" option in the drop-down that appears and entering the corresponding identification number (GenID) in the GenBank. The genes whose expression can be analyzed with the microarray, its corresponding identification number in the GenBank, as well as the oligonucleotides present in the microarray to be used as probes to analyze the expression of said genes appear later in Table 1.. Oligonucleotide probes that represent each gene. For each of the 534 genes related to hematological malignancies, as well as for the genes corresponding to β-actin, glyceraldehyde-3-phosphate dehydrogenase, 18S rR A and 28S rR A, it was searched in GenBank (www.ncbi.hlm. nih.gov/Genbank/) the mR A sequence. An oligonucleotide (probe) specific for each of the selected genes was designed from the GenBank sequence. In some genes, several oligonucleotides located in the 5 'and 3' regions of the gene were designed to analyze the integrity of the mRNA. To ensure specificity in the design of the probes the following criteria were taken into account: Length of the probe to ensure that all probes are going to have a similar behavior, GC content of the probe between 40 and 60% This feature also it was taken into account to ensure that all the probes are going to have a similar behavior Localization in the gene. Probes located at less than 3,000 nucleotides were selected from the 3 'end (poly (A)) of the selected mRNA sequence. Sense of the probe. A "sense" sense strand was chosen, that is, the sequences of the oligonucleotides coincide with sequences of fragments of the corresponding mRNA, instead of being sequences complementary to said fragments. This decision implies that the marked genetic material has to be antisense, "antisense" (complementary to "sense"). Specificity of the probe. To avoid non-specific hybridization, probes that had a percentage of homology were selected, calculated using the BLAST tool (available on the website http://www.ncbi.nlm.nih.gov/), less than 70%. The data on the oligonucleotides used as probes, the identification number of their corresponding sequence in the attached list, as well as data (GenBank identification number, usual abbreviation and name) of the genes for the detection of which expression have been designed Oligonucleotides are shown below in Table 1.

Table 1.- Oligonucleotides used as probes to detect the expression of human genes Among these genes, four of them (ACTB, GAPD, 18S rRNA and 28S rRNA), did not have a special relationship with neoplasms and were initially included in the microarray because, for a long time, it was believed that their expression was constant and They used to normalize the microarray data: they are the type of genes alluded to when talking about "constitutive" genes in other points of memory. At present, it is not believed that there are gene whose expression remains constant in any circumstance, so in the present study the genes ACTB, GAPD, 18S rRNA and 28S rRNA have received the same treatment as the rest of the genes of the microarray , except for the fact that the first two have been used as integrity controls, as described below.

In Table 1 it can be seen that there are genes that are represented by more than one oligonucleotide. This is so because the existence of two or more probes per gene can used to measure the integrity of the synthesized cRNA. The genes for which more than one oligonucleotide was designed to act as a probe, each of which hybridizes with a different sequence, are listed below in the Table 2 · Table 2. - Genes represented by more than one oligonucleotide as a probe Abbreviation Sondal Sonda2 Sonda3 usual of the gene ABL1 SG10 SG180 BCR SG169 SG170 CBFB SG189 SG526 CD28 SG403 SG404 EIF4E SG293 SG305 ELF1 SG512 SG535 SG502 ETS2 SG95 SG537 GCET2 SG504 SG509 SG509 MAFB SG258 SG545 MTCP1 SG358 SG359 SG357 SG366 SG367 RGS1 SG56 SG409 S100A2 SG35 SG71 SNRPB SG142 SG143 SG143 SG777 SG559 SG468 SG468 SG700 SG7 SG73 SG276 SG276 SG276 SG279 SG279 SG279 SG330 SG330 SG330 SGY2 SG330 CD4 SG228 SG229 ACT2 SG463 SG464 GAPD SG464 SG467 Establishment of control probes To reduce the variability, a large number of controls were included in each "microarray". These controls suppose an objective measurement on the quality of the process and therefore, of the quality of the data obtained. They are of several types and origins: a) Probes used as integrity controls These probes were 2 pairs of oligonucleotides complementary to the 5"and 3 'ends of the genes, β-actin (probes code SG463 and SG464) and glyceraldehyde-3 phosphate dehydrogenase (probe codes SG466 and SG467) The relationship between the intensities of the probe located at the 3 'and 5' end allows to check the quality of the starting RNA and the functioning of the labeling reaction The details on these oligonucleotides appear in Table 3. Table 3. - Oligonucleotides used as integrity controls b) Probes used as negative controls These probes are formed mostly by a group of 50 nucleotide (50-mer) oligonucleotides that are not complementary to any known human sequence. For them, the BLAST tool was applied to these probes and it was found that they did not hybridize with any human sequence. They are identified with the SCI codes (SEQ ID NO: 564), SC2 (SEQ ID NO: 565), SC3 (SEQ ID NO: 566), SC4 (SEQ ID NO: 567), SC5 (SEQ ID NO: 568), SC6 (SEQ ID NO: 569) and SC7 (SEQ ID NO: 570) and oligonucleotides SCN1 (SEQ ID NO: 571), SCN5 (SEQ ID NO: 575), SCN7 (SEQ ID NO: 577) and SCN10 (SEQ ID NO: 580). They are used to determine the optimal conditions for hybridization, washing and development of the "chips" or "microarrays". The appearance of the signal associated with them indicates the existence of nonspecific hybridization. c) Exogenous probes used as internal positive controls: "Spiked controls" The "Spiked controls" are synthetic oligonucleotides whose sequence matches a transcript fragment of a non-human gene or any other nucleotide sequence of low homology with gene transcripts human that is polyadenylated in 3 ', which are used as positive control, in the determination of the quality of the process, in the normalization of data and for the establishment of the linear range of the process (Benes V et al., 2003). For this, the corresponding polyadenylated transcripts or sequences are added to the total starting RNA before beginning the labeling process and, therefore, undergo the same reactions (labeling, hybridization and development) as the total RNA of the samples. 7"Spiked controls" were used. To assure low homology with human genes, we used 5 transcripts of Bacillus subtilis genes (dap, thr, trp, phe and lys) and 2 transcripts of the Sharka virus genes, which are frequently referred to by their English designation. "Plum poxvirus" (Sppv), which is a plant virus. The details on these oligonucleotides are shown below in Table 4. The numbers of ATCC (American Type Culture Collection) appearing after the name of the genes of provenance refer to the identification number in the ATCC of strains of E. coli that they contain recombinant plasmids that contain the sequence of the genes from which the transcripts that are added to the RNA are obtained and which were also used for the design of the sequences of the corresponding oligonucleotides attached to the microarray.

Table 4.- Oligonucleotides used as "Spiked Controls" Concentra Size (pM) Oligo- SEQ ID Gene of Code transcr in nucleot NO: origin GeneBank ito solution idos (nt) de "spiked controls " Dap SEQ ID SSPC1 (ATCC L38424 1820 2000 NO: 584 no.87486) Lys SEQ ID SSPC2 (ATCC X17013 1000 1250 NO: 585 no.87482) Thr SEQ ID SSPC3 (ATCC X04603 1980 5 NO: 586 no.87484) SSPC4 SEQ ID Plum pox AF401296 100 NO: 587 virus, isolated PENN2 (Sppvl) Plum pox potyvirus, SEQ ID SSPC5 protein mRNA X57975 750 NO: 588 cover (Sppv2) Phe SEQ ID SSPC6 (ATCC 24537 1320 1000 NO: 589 no.87483) Trp SEQ ID SSPC7 (ATCC K01391 2500 500 NO: 590 no.87485) c. 1 .: Preparation of the 5"Spiked controls" of Bacillus subtilis The E. coli bacteria with the recombinant plasmids were purchased from ATCC (Rockville, MD, USA) The plasmids (pBluescript II -KS) contained the cloned cDNA of a gene of Bacillus subtilis, with cleavage sites for Notl enzymes at the 5 'end and BamHI at the 3' end and a poly (dA) extension anterior to the BamHI cleavage site.

After reconstituting and letting the cells grow overnight at 37 ° C in LB + ampicillin medium, the plasmid was obtained with the Midipreps kit (Jetstar) following the manufacturer's recommendations. 10 μg of each of the plasmids were linearized by digestion with 30U of Notl restriction enzyme, in the presence of buffer 1XNE3 and 1XBSA for 3 hours at 37 ° C. Linearized plasmids were extracted with phenol: chloroform: isoamyl alcohol (Ambion), precipitation with 0.1 vol of 3M sodium acetate (Sigma) and 2.5 vol of 100% Ethanol and elimination of salts with 80% Ethanol, following the protocol described above. The DNA obtained was resuspended in 10 μ? of RNasas free water. Next, we proceeded to the synthesis of the sense transcripts with a Transcription in vi tro reaction (I.V.T) from 1 μg of linearized plasmid using the MegaScript T3 kit (Ambion) and following the manufacturer's recommendations. The obtained plasmids were purified with the RNeasy Total RNA Isolation Kit (QIAGEN), following the manufacturer's recommendations. We proceeded to the quantification, determination of the purity, quality and size of the transcripts obtained following the same procedures that are described later for the total RNA. c.2. Preparation of the 2"Spiked controls" representing SPPV genes The recombinant plasmids (Progenika Biopharma) contained the cloned cDNA of the two sppvl and sspv2 genes inserted between two restriction sites PvuII and PstI. The 3 'end of each insert contains an extension of polyadenylation. JM109 cells were transformed with the plasmids containing the transcripts. The cells were grown on plates with LB + ampicillin medium at 37 ° C, the colonies were selected with the transformed cells and grown in liquid medium LB + A P. The recovery of the plasmids was carried out with the Midipreps Plasmid Purification kit (Qiagen ), following the manufacturer's recommendations. 10 μg of each plasmid was linearized with 30U of the restriction enzyme PvuII. The insert was extracted with phenol: chloroform: isoamyl alcohol (Ambion), precipitation with 0.1 volume of 7.5 M sodium acetate and 2.5 volumes of 100% ethanol. The salts were removed by two washes with 80% ethanol. The DNA obtained was resuspended in 10 μ? of RNasas free water.

Next, we proceeded to the synthesis of the transcripts from 1 / xg of each linearized plasmid with the MegaScript T7 kit (Ambion) and following the manufacturer's recommendations. The product of the reaction was cleaned with the RNeasy Total RNA Isolation Kit (Qiagen). Then we proceeded to the quantification, measurement of the purity of the transcripts obtained and checking their size. From the obtained transcripts, a "Spiked controls" solution was prepared with different concentrations of each of the "spiked" (see Table 3), so as to cover the entire range of intensities of the "scanner" reading system (intensity values ranging from 0 to 65,535 in arbitrary units). This solution was added in the same amount to 5. g of total starting RNA of each sample before beginning the process. c.3. Design of representative probes of each one of the transcripts: For the behavior of the probes to be as close to the behavior of the probes designed for the genes to be studied, with the Oligo 6.0 (.BI) program they were selected for each "Spiked control" "those sequences that fulfilled the same requirements established for the probes of the genes represented (length, GC content," sense "strand and distance to the 3 'end) and that did not form stable curls (energy lower than -7 Kcal / mol). The BLAST tool was applied to the sequences that met these requirements and the one with the least homology with human sequences was chosen. After depositing and immobilizing the probes corresponding to the "Spiked controls" on the glass, it was found: a) that the probes did not hybridize non-specifically with the samples to be analyzed, b) that all the probes had similar hybridization characteristics, and ) that the intensity signal obtained from each of them could be related to the amount of transcript added to the RNA. d) Hybridization controls Synthetic DNA oligonucleotides of 70 nucleotides (70-mers) modified at one end with a biotin molecule were used as hybridization controls. These molecules are added in the same amount to the sample just before the hybridization, so that their value depends only on the processes of hybridization, development and capture of images of the "microarray". For each of these 70-oligonucleotides, there are on the "microarray" several copies of an oligonucleotide 50 nucleotides in length (50-mer), complementary to the corresponding oligonucleotide 70 -mer with which it must anneal. Oligonucleotides which are part of the microarray and which are complementary to oligonucleotides 70 -mixers that are added to the cRNA before hybridizing are those of the codes SCN2, SCN3, SCN6, SCN8, SCN11, SCN12 and SCN13. To ensure low homology with human sequences, the sequences of these oligonucleotides were obtained from sequences of Arabidopsis thaliana and Trypanosus a brucei. Its characteristics appear in Table 5 Table 5.- Oligonucleotides used in the microarray as positive hybridization controls Oligonucle SEQ ID NO: Oligonucleotide Code Gene 50- GenBan 70 origin -array origin- present k complement in Aryan the microarray SCN2 SEQ ID Alpha-1, 4- AY0269 C2 NO: 572 fucosiltransfera 41 sa (FT4-M) of Oligonucle SEQ ID NO: Oligonucleotide Code Gene 50- origin of genus ear GenBan 70 -e present k complement in Aryan Arabidopsis thaliana microarray SCN3 SEQ ID mRNA of the AJ2391 C3 NO: 573 thioredoxin of 28 Trypanosoma brucei SCN6 SEQ ID mRNA of a AY0510 C6 NO: 576 purported 79 - rubisco expression protein (complete CDS) of Arabidopsis thaliana SCN8 SEQ ID mRNA of a AY0458 C8 Oligonucle SEQ ID NO: Oligonucleotide Code Gene 50- GenBan 70 origin of provenant-present k complement in Aryan the microarray NO: 578 purported 79 lipid transfer protein (Atlg48750) (complete CDS) of Arabidopsis thalian-a SCN11 SEQ ID mRNA of a AY0458 CU NO: 581 purported 79 lipid transfer protein. (Atlg48750) (complete CDS) of Oligonucle SEQ ID NO: Oligonucleotide Code Gene 50 - origin of pure Orthid GenBan 70 - present k complement in Aryan the microarray (complete CDS) of Arabidopsis thaliana For the design of the 50-mer oligonucleotides it was found, in a similar way to that previously described for the "Spiked controls", that the oligonucleotides to be used did not hybridize non-specifically with the samples to be analyzed, that all the probes had characteristics of similar hybridization and that the intensity signal obtained from each of them could be related to the amount of the corresponding 70-mer oligonucleotide added to the cRNA. This allowed the oligonucleotides indicated in Table 5 to be considered valid. The oligonucleotides SCN4 (SEQ ID NO: 574) and SCN9 (SEQ ID NO: 579), designed in principle to act as hybridization controls, were found to produce specific hybridization. when hybridizing human cRNA, so they also appear in the microarray, as if they were probes representing some human gene, but are not taken into account as positive hybridization controls. For their part, the oligonucleotides SCN1 (SEQ ID NO: 571), SCN5 (SEQ ID NO: 575), SCN7 (SEQ ID N0: 577) and SCN10 (SEQ ID NO: 580), which also did not hybridize non-specifically with the Samples, are also present in the microarray as negative hybridization controls, to the river added to the cRNA no oligonucleotide complementary to them. On the other hand, the solution of hybridization controls containing oligomericides 70-complementary to the 50-mer oligonucleotides present in the microarray as positive hybridization controls prepared from the corresponding biotinylated 70-mer sequences using for each of them a different concentration, as shown in Table 6: Table 6. - Composition of the solution of white Hybridization positive controls The dimethylsulfoxide (DMSO) was used as a target without any probe, since this is the solvent in which the oligonucleotides were located when they were deposited on the surface. of the microarray. Description of the device "microarray" Twelve replicas of each probe are deposited in different locations on the surface of a solid support (glass similar to that of the microscope slide holders) using Microgrid II Spoter (Biorobotics). The 12 replicas of each probe were distributed on the random support: 6 in the upper area and 6 in the lower area. As a solid support, aminosilanized glasses (Corning) were used. Humidity and temperature were controlled throughout the printing process. The covalent attachment of the probes to the solid supports was carried out by cross-linking by ultraviolet radiation using the "Stratalinker" device (Stratagene). The quality control of the production process of the "microarrays" was as follows: a) In each production batch, a "microarray" was stained with Ethidium Bromide, which allows analyzing the size and shape of the printed spots, b) Other "array" of each batch was hybridized with an already hybridized cRNA, analyzing the hybridization signal, the background noise and the reproducibility of the replicas. The characteristics of the "array" are shown below in Table 7: Table 7. - Characteristics of the microarray Number of genes represented 538 Length of the oligonucleotides 25-55 meros Strand analyzed Sense Number of oligonucleotides porl (except 21 genes that gene are represented by 2 or 3 different oligonucleotides) Number of replicates of each 12 oligonucleotide White DMSO Integrity controls 4 Spiked controls (internal positive controls 7 ) Positive controls of 9 hybridization Negative controls 11 Total number of points 8192 (32 areas x 16 xl6) Size of the microarray 25 x 75 mm Area of spraying 16.38 x 17.82 mm Distance between point x- and - axis 360μp? Treatment of samples Cell cultures Cell cultures Jurkat (cell line from Leukemia T) and U937 (cell line from Promonocitic Leukemia) were centrifuged for 10 minutes at 1200 rpm and, after decanting the supernatant, the precipitate was resuspended in RNAlater (Ambion Inc) and stored at -80 ° C until the moment of RNA extraction. The RNA was extracted with TRIzol (Gibco-BRL Carlbad, CA, USA) following the manufacturer's recommendations. Blood samples Blood samples were collected directly on PAXgene Blood RNA Tubes -PreAnalytix tubes (Qiagen). 2.5 ml of blood was drawn into each tube and two tubes per individual. The tubes were inverted several times to allow the blood to mix with the stabilizing liquid contained in the tube, and were stored at -20 ° C until the night before RNA extraction Total RNA extraction The tubes with the sample were incubated at room temperature overnight prior to RNA extraction. For the extraction, the PAXgene Blood RNA kit (Qiagen) was used following the manufacturer's recommendations, including the intermediate step of DNase treatment (RNase-Free DNase Set, Quiagen) in column. The RNA of each extraction tube was eluted in 80 μ? of buffer BR5. The RNA of the two tubes that corresponded to each patient was joined in a single tube. Purification of total RNA To ensure that the RNA obtained is free of contaminants that may interfere with subsequent labeling reactions, it was purified as follows: a 160 μ? of total RNA solution was added 16 μ? (0.1 vol) of 7.5 M sodium acetate (Sigma) and 400 μ? (2.5 vol) of 100% ethanol. The solution was mixed on a "vortex" shaker and incubated for 1 hour at -20 ° C. After 20 minutes of centrifugation at 12,000 xg at 4 ° C, the precipitate was washed twice with 500 μ? of 80% ethanol and resuspended in 35 μ? of RNasas free water. The RNAs obtained were stored at -80 ° C until their later use. Quantification of total RNA Quantitation of total RNA was carried out by measuring the absorbance at 260 nm in a spectrophotometer (DU 65, Beckman Coulter). 2 μ? of the total RNA solution were diluted in 98 μ? of 1 mM Tris-HCl pH 7.5 and the concentration ^ g / ml was estimated) taking into account that 1 Unit of Optical Density at 260 nm corresponds with an RNA concentration of 44 μg / ml. Determination of RNA purity and quality The degree of purity was established from the ratio of absorbances A260 / A280 (nucleic acid / proteins), considering that RNA is adequate, of "good quality", when the A260 / A280 ratio it is between 1.9 and 2.1. The quality of the total RNA was determined by visualization of the RNA after electrophoresis. 500 ng of total RNA were subjected to electrophoresis in 1% agarose gel (FMC) in TAE lx buffer with BrEt (0.5 mg / ml), under a potential difference of 100V for 25 minutes in current electrophoresis cuvettes continue (BioRad). The phage f 29 digested with the restriction enzyme BamH I was used as molecular weight marker. The gels were visualized in a Gel Doc ultraviolet transilluminator (BioRad). Sample labeling The choice of the strand with direction as a probe limited the labeling strategy to those approaches that produced a marked antisense product (complementary to the probe immobilized on the solid support). Marcaj ea cRNA This type of labeling was performed during the course of an amplification process consisting of the use for the synthesis of single-stranded cDNA, of an oligo primer (dT) containing a promoter for the enzyme RNA polymerase of phage T7, enzyme which will be used in the amplification stage of the sample. a.- cDNA synthesis: stage in which complementary DNA (cDNA) was synthesized to the starting mRNA. 5 μg of RNAtotal was incubated with 2 μ? of the "Spiked controls" solution and 100 pmol of primer T7- (dT) 24 (Genset Corp) in a final volume of 12 μ? for 10 minutes at 70 ° C in a thermoblock, the mixture was cooled on ice and 4 μ? of buffer 5X First Strand Buffer (Gibco BRL Life Technologies), 2 μ? DTT 0, 1M (Gibco BRL Life Technologies), 1μ? dNTP mix lOmM (Gibco BRL Life Technologies) and 1 μ? of SuperScript II RNase H RT (200 U / μ?) (Gibco BRL Life Technologies). After 1 hour of incubation in a bath provided with a thermostat (Selecta) at 42 ° C, the reaction was cooled on ice. b.- Synthesis of double-stranded DNA (dsDNA): a double strand of DNA was synthesized from the cDNA synthesized in the previous step. At 20 μ? of previous reaction was added 91 μ? of water free of RNasas, 30 μ? of "Second Strand Reaction buffer" (Gibco BRL Life Technologies), 3 μ? dNTPs lOm (Gibco BRL Life Technologies), 10 U E. coli DNA Ligase (Gibco BRL Life Technologies), 40 U E. coli DNA polymerase I (Gibco BRL Life Technologies), 2 U E. coli RNase H (Gibco BRL Life Technologies ) in a final volume of 150 μ? . The reaction was incubated in a thermoblock at 16 ° C for 2 hours. Then 10U of T4 DNA Polymerase (Gibco BRL Life Technologies) was added and the mixture was incubated for 5 minutes at 16 ° C. To stop the reaction, 10 μ? of 0.5 M EDTA. c- Purification of dsDNA: To eliminate possible product residues from reactions that could interfere with subsequent labeling reactions, the obtained DNA was purified through phenol / chloroform extraction and subsequent precipitation. At 162 μ? of previous reaction was added 162 μ? of phenol solution: chloroform: isoamyl alcohol (25: 24: 1) (Ambion). Centrifuged for 2 min at 12,000 xg in a centrifuge at room temperature, the upper aqueous phase was collected. To this upper phase were added 0.5 volumes of 7.5M ammonium acetate (Sigma Chemical) and 2.5 volumes of 100% Ethanol cooled to -20 ° C). After vortexing to mix the components well and centrifugation for 20 minutes at 12000 xg at room temperature, the supernatant was removed and the precipitate was washed twice with 80% ethanol. The DNA obtained was resuspended in 10 μ? of RNase-free water and concentrated in a "Speed-Vac" concentrator up to a volume of 2 μ? . This DNA was stored at -20 ° C until its later use. d.- Synthesis and labeling of cRNA: This reaction was carried out in a volume of 20 μ? and using the T7 Megascript kit (Ambion), following the manufacturer's instructions and incorporating nucleotides modified with biotin, Bio-ll-CTP and Bio-11 UTP (Perkin Elmer) in an unmodified nucleotide / modified nucleotide ratio 1: 3. The reaction was incubated for 5 h and 15 minutes in a thermostat bath (Selecta) at 37 ° C, stirring the reaction every 45 minutes. After this incubation 1 μ? of DNAse and incubated 30 min at 37 ° C. e. -Purification of the biotinylated cRNA: The biotinylated cRNA was purified with the RNeasy Total RNA Isolation Kit (Qiagen) following the manufacturer's instructions. The obtained biotinylated cRNAs were eluted in a volume of 80 μ? and stored at -80 ° C until later use. The quantity, purity and quality of the obtained cRNA were determined following the same procedures as described for total RNA. The cRNA was stored at -80 ° C until its later use. Fragmentation of biotinylated cRNA 10 g of biotinylated cRNA were fragmented in the presence of 5x fragmentation buffer (200 mM Tris-acetate, pH 8.1, 500 mM HOAC, 150 mM MgOAc) for 35 minutes at 94 ° C in a thermoblock. It was found that the fragmentation reaction had been carried out correctly by visualizing 1 μ? of fragmentation solution in electrophoresis on 1% agarose gel. Hybridization of the labeled cRNA with the probes of the "microarray" In this step, the labeled genetic material was put in contact with the probes immobilized on the solid support.

To the solution of biotinylated and fragmented cRNA was added 10 μ? of the solution of hybridization controls and the mixture was incubated 3 min at 95 ° C to denature the possible secondary structures. After incubation the mixture was immediately taken to ice to prevent possible renaturation of the sample. Hybridization was carried out for 6 hours at 42 ° C in the Ventana Discovery hybridization window (Ventana Medical Systems). Hybridization and washing buffers were supplied by Ventana Medical System. The "microarrays" were automatically stained in the own hybridization station with streptavidin conjugated with Cy3 (Amersham Biosciences) using the manufacturer's recommendations. Image capture and quantification of the "microarrays" After the hybridization and development, the images of the "microarrays" were identified and analyzed by the ScanArray 4000 confocal fluorescent "scanner" (Perkin Elmer) equipped with a green laser (543 nm to excite the Cy3 fluorophore). The "software" used was ScanArray 3.1.

The use of the software QuantArray 3.0 (Perkin Elmer) provided the absolute values of the intensity of hybridization and background noise as a function of the light emitted by the Cy3 in each probe in an Excel format. Data analysis: Preliminary processing First of all, the value of background noise was subtracted from the absolute intensity values of all the oligonucleotides. For this, absolute intensity values and background noise values were used, which the program used to convert the fluorophore signals automatically returns for each of the microarray points: the corresponding intensity value is obtained from The zone that has been defined as a point and the value of the background noise is obtained from the area around the point. Next, the average level of intensity of hybridization of each of the oligonucleotides of the microarray was calculated from the bounded average of the intensities of the 12 replicas of each of the oligonucleotides. To do this, the higher values and the lower values of the distribution of points of hybridization signal values obtained with each of the replicas of the same oligonucleotide must be eliminated before calculating the average. The calculation was made using the Microsoft Excel program and, specifically, the function .ACOTED MEDIA of the same, in which the parameter "percentage" was set at 0.2, which means to set the percentage of values eliminated in 20% of the higher values and 20% of the lower values; the function rounds the number of data points excluded to the nearest 2's multiple. Finally, in order to determine the validity of a hybridization, it is necessary that a series of established criteria be met: 1) the ratio between the mean intensity and the average background of all the oligonucleotides of the chip is greater than 10; 2) the value of the mean variation coefficient (standard deviation of the replicates versus the mean of the replicates) of all the oligonucleotide replicas of the chip must be less than 0.3; 3) the mean value of the negative control must be less than 2.5 times the value of the average DMSO; 4) Sl must be obtained both in the hybridization controls and in the exogenous internal positive controls (Spiked controls). The analysis of the data was done in R, version 1.9.1.

R is a programming language in which both classical and modern statistical techniques can be applied (R Developmental Core Team, 2004, http://www.R-project.org), which has a series of functions stored in packages for the manipulation, calculation and graphic representation of data (Venables et al., 2004). There are hundreds of packages written by different authors for R, with special statistical functions or that allow the access and manipulation of data and are available for download from the CRAN web pages (http: // were. R-proj ect. Org /) o Bioconductor (http://www.bioconductor.org). In some specific cases, the commercial statistical analysis software SPSS (Chicago, USA) was also used. EXAMPLE EXAMPLE 1.- RESULTS OBTAINED BY USING THE "MICROARRAY" DEVICE WITH U937 CELL SAMPLES VS JURKAT CELLS In order to know if the device allows to differentiate two cell lines, they were hybridized in 10 microchips: 5 samples of biotinylated cRNAs synthesized following the protocol optimized work, obtained from RNA of U937 cells (cell line from Promonocitic Leukemia) and 5 samples of biotinylated cRNAs obtained from RNA of Jurkat cells (cell line from Leukemia T). The initial steps of preliminary processing of the data and validation of the hybridization mentioned above were carried out in the section on "Data analysis: Preliminary processing" and then standardization and filtering of data was carried out: - Data normalization. We used the method "variance stabilization normalization", available in the package "vsn" of R. There are different packages available on the Internet for R, with special statistical functions or that allow access and manipulation of data and are available for download from CRA (http: // were. r-proj ect. org /) or Bioconductor (http: // www. bioconductor.org) - Data filtering. Two filtering operations were performed with the "Filterfun" function of the "Genefilter" package of R. The genes that did not overcome either of the two filters were not used in the data analysis. The filters carried out were: Filtering to exclude genes with an intensity value close to DMSO. This filter allowed to work with genes with intensity value less average background noise greater than 550 arbitrary units (approximately 2 times the value of the DMSO). Filtering to exclude genes with minimal intensity variation throughout the samples. We worked with genes with interquartile range of normalized intensity throughout the samples greater than 0.3. Data filtering left 83 probes that constituted the work list. With them a grouping of the unsupervised samples was carried out, which are those groupings in which the structure of the data is not known in advance, the system learning how the data are distributed between classes based on a distance function. With the grouping a tree or hierarchical grouping was obtained, in which the samples are grouped according to their similarity in the expression of certain genes, those corresponding to the oligonucleotides of the work list, so that the closest samples are those with a similar expression profile. The grouping was performed with the hclust function of the stats package of R. The unsupervised analysis of the 10 samples produced their separation into two groups or main branches depending on the cell type to which the samples belong: a group contains the 5 hybridizations made to from U937 cells and the other group contains the 5 hybridizations made from Jurkat cells. The tree resulting from this unsupervised grouping is shown in part A of Figure 1. Next, to know if there were statistically significant differences between the two groups of samples, the "Step-down maxT multiple testing procedures" method was used. maxT), which is an application of the mt.maxT function of the multtest package of the Bioconductor R software, which applies a statistical test and performs a strong control over the false positive rate. To this function it is necessary to provide: a) Values on which the statistical test is to be applied, in this case, on the normalized values of the 83 oligonucleotides that exceeded the filters b) Groups from which one wants to look for differences, in this case the 5 samples of Jurkat cells versus the 5 samples of U937 cells c) Number of permutations to be performed. In this case, 100,000 permutations were made. d) By default, the Welch test was chosen to specify the statistical tool to be used to test the hypothesis of no association between variables and class labels. The application of this analysis with a p <value; 0.001 provided a list of 69 statistically significant probes between the two groups, which are the following: SG12, SG20, SG23, SG24, SG38, SG39, SG45, SG49, SG53, SG59, SG60, SG62, SG76, SG78, SG89, SG92, SG94, SG102, SG474, SG478, SG487, SG114, SG120, SG140, SG142, SG145, SG150, SG154, SG158, SG174, SG175, SG194, SG195, SG211, SG230, SG231, SG235, SG260, SG264, SG266, SG268, SG270, SG272, SG282, SG294, SG308, SG311, SG330, SG332, SG333, SG339, SG344, SG364, SG403, SG423, SG434, SG456, SG506, SG513, SG514, SG515, SG524, SG533, SG538, SG541, SG559 Once the statistically significant genes were known to distinguish between the two groups of samples (which would be the genes corresponding to the probes identified as statistically significant), the supervised grouping of the samples was carried out according to the signal intensity of the 69 probes. statistically significant obtained. The term "supervised", applied to a grouping, refers to the fact that the structure of the data is known in advance, which allows using the previous information; with this, after a training process that allows the system to learn to distinguish between classes, the network can be used to assign new members to the predefined classes. In this case, the supervised grouping of the samples according to the intensity of the signal obtained with the 69 statistically significant probes obtained, is again a tree that is divided into two main branches depending on the cell type to which the samples belong. The tree obtained with the supervised grouping is shown in part B of Figure 1. EXAMPLE 2.- RESULTS OBTAINED BY USING THE "ARRAY" DEVICE WITH SAMPLES OF HEALTHY SUBJECTS VS C937 AND JURKAT CELLS The expression of 5 cell samples was compared U937 and 5 samples of Jurkat cells against the expression of 10 samples from whole blood of healthy subjects. In a manner similar to that described in Example 1, the steps of initial processing of the data, validation of the hybridizations, normalization and filtering were carried out. A total of 180 genes exceeded the filtering processes. The unsupervised grouping of the samples (performed with the hclust function of the stats package of R applying Pearson correlation) based on the expression of the 180 genes provided a tree with two main branches: one branch contains all the samples from cell cultures and the other branch contains all samples from whole blood of healthy subjects, so it is shown that the tool is able to find differences in expression.

The tree obtained after performing this unsupervised grouping is shown in part A of Figure 2. The maxT test (p <0.001) was performed to search for genes with statistically significant differences between samples from U937 and Jurkart cell cultures and 10 samples from whole blood of healthy subjects. The statistical analysis provided a list of 131 probes with statistically significant differences between both groups of samples. They are the following: SG1, SG4, SG7, SG8, SG10, SG13, SG15, SG16, SG17, SG18, SG19, SG20, SG26, SG29, SG30, SG34, SG36, SG39, SG42, SG44, SG49, SG51, SG52, SG58, SG64, SG65, SG67, SG76, SG77, SG80, SG84, SG86, SG89, SG92, SG93, SG94, SG98, SG99, SG101, SG102, SG107, SG463, SG464, SG474, SG475, SG485, SG487, SG466, SG467, SG471, SG472, SG473, SG120, SG129, SG138, SG141, SG144, SG145, SG147, SG158, SG163, SG164, SG176, SG185, SG186, SG197, SG207, SG208, SG217, SG227, SG231, SG265, SG266, SG277, SG278, SG283, SG285, SG299, SG307, SG308, SG311, SG313, SG318, SG319, SG328, SG333, SG336, SG342, SG344, SG357, SG361, SG376, SG384, SG389, SG395, SG398, SG403, SG404, SG407, SG416, SG420, SG423, SG430, SG436, SG446, SG455, SG461, SG489, SG491, SG492, SG493, SG498, SG500, SG504, SG505, SG506, SG514, SG516, SG517, SG520, SG526, SG530, SG533, SG538, SG545, SG547, SG554, SG555, SG558. The grouping of the 20 samples based on the expression of the statistically significant probes found gave rise again to a tree with two main branches, one corresponding to the samples from cell cultures and another corresponding to the samples from healthy individuals. Said grouping appears in part B of Figure 2. EXAMPLE 3. - RESULTS OBTAINED WITH SAMPLES OF PATIENTS WITH CHRONIC LYMPHEAL LEUKEMIA (LLC) VS CELLS U937 AND JURKAT The expression profiles of samples from cell cultures U937 and Jurkats were compared with 26 samples from whole blood of subjects with CLL. The samples were subjected to a preliminary processing of the data, normalized and filtered analogously to that used in Examples 1 and 2 and a total of 236 probes exceeded the filters. The unsupervised grouping of the samples based on the expression of the probes that exceeded the filters showed a tree with two main branches: one containing the samples of cell cultures and the other the LLC samples. Said tree is shown in part A of Figure 3. The maxT test (p <0.001) was applied to search for genes with statistically significant differences between the two groups of samples. This analysis provided a list of 120 probes. These are the following: SG2, SG4, SG8, SG10, SG13, SG15, SG16, SG19, SG20, SG23, SG26, SG28, SG31, SG34, SG36, SG39, SG48, SG58, SG60, SG65, SG76, SG77, SG84, SG89, SG94, SG9, SG97, SG99, SG102, SG106, SG107, SG463, SG464, SG474, SG475, SG481, SG465, SG487, SG487, SG466, SG467, SG471, SG473, SG115, SG116, SG117, SG120, SG129, SG134, SG135, SG138, SG139, SG141, SG145, SG158, SG161, SG163, SG176, SG178, SG185, SG207, SG208, SG210, SG217, SG227, SG231, SG237, SG264, SG272, SG277, SG281, SG283, SG286, SG294, SG298, SG299, SG307, SG308, SG319, SG328, SG330, SG333, SG336, SG342, SG344, SG345, SG347, SG361, SG384, SG389, SG395, SG404, SG407, SG416, SG423, SG428, SG430, SG432, SG434, SG444, SG446, SG453, SG458, SG459, SG491, SG498, SG507, SG508, SG511, SG517, SG518, SG522, SG526, SG530, SG533, SG538, SG541, SG554, SG558, SG561. The grouping of the 30 samples based on the expression of the 120 statistically significant probes gave rise again to a tree with two main branches, one corresponding to the cell culture samples and the other corresponding to the samples taken from subjects suffering from CLL. . This tree is shown in part B of Figure 3. - EXAMPLE 4: RESULTS OBTAINED WITH SAMPLES OF HEALTHY SUBJECTS VS PATIENTS WITH CHRONIC LYMPHEAL LEUKEMIA (LLC) 68 hybridizations that met the quality criteria from 68 samples from different healthy subjects and with clinical diagnosis of CLL were divided into 2 groups: Training Group used to obtain the functions of the classifier and Test Group, used to test the obtained classifier. The Training Group consisted of 30 samples (10 from healthy subjects and 20 from CLL subjects) and the Test Group consisted of 38 samples (5 samples from healthy subjects and 33 samples from subjects with CLL). In order to obtain the classification function, we worked with the results obtained from the Hybridizations of the Training Group. The steps taken to obtain the classification function were: - Data normalization. We used the "variance stabilization normalization" method, available in the "vsn" package of R. - Data filtering. Two filtering operations were performed with the "Filterfun" function of the "Genefilter" package of R. The genes that did not overcome either of the two filters were not used in the data analysis. Of the 588 oligonucleotides on the chip, 224 exceeded the 2 filters and constituted the working list. 2. Filtering to exclude genes with an intensity value close to DMSO. This filter allowed to work with genes with intensity value less mean background noise greater than 550 arbitrary units (approximately 2 times the value of DMSO) in more than 25% of the 30 samples (7 samples) that make up the Training Group. 3. Filtering to exclude genes with minimal intensity variation throughout the samples. We worked with genes with interquartile range of normalized intensity throughout the samples greater than 0.3. Two classification systems were used: 4.1.- Construction of a classification system with PAM.

To identify groups of genes that best characterize each type of sample and to check the classification rate of these groups of genes, Prediction Analysis for Microarrays (PAM) was used as a "pamra" package of R. It is a statistical technique that identifies a group of genes that best characterize a predefined class and use this group of genes to predict the class to which new samples belong. PAM uses a modified version of the "nearest centroids" classification method (Tibshirani et al., 2002) called "Nearest Shrunken Centroids". A validation called "10 fold cross validation" was carried out, consisting of building the model with 90% of the samples and trying to predict the class of 10% of the samples that have not intervened in the construction of the model. This procedure is repeated 10 times and the classification error of 10% of the samples is put together to calculate the overall error. This error reflects the number of poorly classified samples (Bullinger et al., 2005). 4.1.1. Construction of the model. Based on the filtered and normalized data of the 30 samples that make up the Training Group, arbitrarily assigning the Group of Health to Group 0 and the Group of LLC to Group 1, performing the 10 cross-validations and with a threshold value of Delta 3.1. The obtained model was formed by the following oligonucleotides: SG459, SG428, SG507, SG508, SG117, SG237. The coefficients of the classifier corresponding to each of these oligonucleotides are shown below in Table 8: Table 8.- Coefficients of the PAM classifier 4. 1.2. Validation of the PAM classifier. The cross-validation of the samples that make up the Training Group correctly classified 28 of the 30 samples.

From the filtered and normalized data of the 38 samples that make up the Test Group, values of probability p belonging to group 0 (healthy group) or group 1 (group LLC) were obtained. The higher the value of p, the greater the likelihood of belonging to that group. It has been considered that values greater than 0.5 indicate belonging to that group. The p values obtained for each sample are indicated in Table 9. Table 9.- Probability values obtained with the PAM classifier for the Test group Sample p (Group p (Group1) £ 1 LLC236 0.48893545 0, 5106455 LLC240 0, 3807527 0, 6192473 LLC168 0, 1616066 0, 8383934 LLC172 0, 2002317 0, 7997683 LLC174 0, 1601147 0, 8398853 LLC175 0, 6009558 0 , 3990442:? Only sample misclassified LLC177 0, 8365815 0, 1634185 LLC179 0, 2300440 0, 7699560 LLC181 0.2177406 0, 7822594 LLC182 0, 3450880 0, 6549120 LLC164 0, 2590083 0, 7409917 LLC159 0, 6311414 0, 3688586 LLC142R 0, 2111712 0, 7888288 LLC105 0, 7037203 0, 2962797 LLC107 0, 3764637 0, 6235363 LLC109 0, 3525788 0, 6474212 LLC112 0, 2059187 0, 7940813 Sample p (Group p (Group1) £ 1 LLC151 0, 2951067 0, 7048933 LLC158 0, 1932882 0, 8067118 LLC169 0, 3525937 0, 6474063 LLC171 0, 1495153 0, 8504847 LLC178 0.2260191 0, 7739809 LLC111 0, 2951168 0, 7048832 LLC155 0, 2832151 0, 7167849 With this model, 37 of the 38 samples of the Test Group are correctly classified: all the samples corresponding to healthy individuals (those whose denomination is headed by the letter "S") have a greater than 0.5 probability of belonging to group 0, while all the samples corresponding to individuals suffering from CLL (which are the samples whose denomination begins with the letters "LLC") minus one have a greater than 0.5 probability of belonging to group 1. 4.2.- Construction of a system of classification with logistic regression. 4.2.1.- Selection of genes with statistically significant differences between healthy and CLL (Training Group). From standard data and filtered as described above, for the selection of genes with significant differences the method "Step-down maxT multiple testing procedures" (maxT) was used, which is an application of the mt.maxT function of the multtest package of the R software of Bioconductor, which applies a statistical test and performs a strong control over the false positive rate. The application of this statistical test, with a value of p <; 0.001, to the 224 oligonucleotides that exceeded the filters, produced a list of 7 oligonucleotides: SG117, SG428, SG459, SG461, SG493, SG507, SG508. The steps used to obtain the list of 7 significant genes between healthy and LLC were: Method that makes permutations and adjusts the values of p resT < -mt .maxT (exprs (224 oligonucleotides that have passed the filters and normalized of the training group), Types of samples in the training group, test = "t", B = 100000): function mt.maxT that through Permutations are adjusting the values of probability (significance), which supposes a strong control of the false positive rate. To this function you must provide 1. - Genes on which you want to apply the statistical test, in this case, on the normalized values of the 224 oligonucleotides that have passed the filters of all the samples included in Training Group 2. - Groups from which you want to look for differences, in this case 10 samples of healthy and 20 samples of LLC. You want to find differences between healthy and LLC 3. - Number of permutations that you want to make. In this case, 100,000 permutations were made. 4. - By default the elch test was chosen as the statistical test The statistically significant genes at the p < 0.001 were selected through this test and a number of 7 was obtained. 4.2.2.- Obtaining the classification function with SPSS. By logistic regression from the normalized values of the 7 statistically significant oligonucleotides obtained from the 30 samples that make up the Training Group and assigning arbitrarily group 0 to the healthy samples and group 1 to the LLC samples, the values of the classification function. The coefficients corresponding to each oligonucleotide were those shown below in Table 10: Table 10.- Coefficients of the classification function calculated by logistic regression From these coefficients, for each sample i calculates a value x ± in the following way: xi = Constant + (Coef ohle0009 * Imni SG117) + (Coef SG428"Imni SG428) + (Coef SG459 * Imni SG459) + (Coef SG461 * Imn SG46D + (Coef SG493 * Imni SG493) + (Coef SG507 * Imn SG507) + (Coef SG508 * Imm SG508). where Imni is the average normalized intensity value of sample i.

A probability value (pi) is calculated from the value xi. The closer the value of pa 0, the greater the probability that the sample belongs to the group of healthy subjects (assigned as group 0) and the closer the value of pa 1, the greater the probability that the sample belongs to the group of subjects LLC (assigned as group 1). The formula used to determine the value of p is: Pi = l / (l + e-xi).

As shown in Table 11, the function obtained correctly classified the 30 samples belonging to the training group. The closer to 0 the greater the likelihood that it will be healthy and the closer it is to the higher probability LLC.

Table 11 : Classifier table? a- The cut-off value is, 500 4.2.3. Validation of the classifier system.- From the filtered and normalized values, as detailed above, the Imni values of the 7 oligonucleotides that make up the classifier of each of the 38 samples that make up the test group were obtained. Results of the validation of the classifier system. Below are tables showing the Imni value of each of the 7 oligonucleotides included in the classifier and the values of xi and i calculated according to the formulas described above, obtained for each of the 38 samples of the test group. Samples that begin with S correspond to healthy subjects and samples that start with CLL are from CLL subjects. 37 of 38 samples are classified correctly. Only the sample LLC175, for which a value of pi = 0 is obtained, is classified incorrectly.

Table 12.- Results obtained with the test group through the classification function obtained by logistic regression 5 20 SG428 6.40 6.98 6.00 6, 24 6.09 6.77 6, 16 7.49 4.85 6.38 SG459 7, 93 8, 54 8, 01 7.55 8, 19 8, 94 8, 86 8, 69 7, 72 8, 78 SG461 6, 77 7.18 6, 89 6.79 6, 54 6, 72 7, 02 7.18 5, 98 6.86 SG493 7, 14 7, 92 7, 72 7.47 6, 73 7, 97 7.96 8.45 7, 16 8, 05 SG507 9.00 8, 98 8.41 8, 74 8.80 9, 53 9.67 10.09 8, 76 9, 76 SG508 7.50 7.43 7.22 7.36 6, 90 8, 08 7.38 7.81 6.43 7.94 Xi 31.80 50.30 12, 52 8, 94 3, 77 67, 59 39, 95 63, 19 -75, 92 59, 42 Pi 1.00 1, 00 1, 00 1, 00 0.98 1.00 1.00 1, 00 0, 00 1.00 fifteen twenty SG507 9.40 9, 53 9.37 8, 86 9.34 9.49 9.49 9.11 8.89 9.41 SG508 8, 17 8, 18 7.37 7, 80 7, 81 7, 97 7.75 7.79 7.46 7, 69 i 62, 78 73, 52 19.76 53, 03 28, 34 68, 92 33.36 17.23 16.00 45.86 Pi 1.00 1, 00 1, 00 1.00 1, 00 1, 00 1, 00 1.00 1.00 1.00 fifteen twenty A third group of 40 samples was formed. For this, hybridization or labeling replicas were used (samples whose denomination begins with S and Strans are samples from people considered healthy and those starting with LLC samples from patients with chronic lymphatic leukemia). Ester group of samples was used for the validation of the classification system. The data was normalized as described above. The results of the classification are shown in Table 13. 40 of the 40 samples are correctly classified.

Table 13.- Results obtained in the validation of the classification function obtained by logistic regression Imni S120.7 S120.14 Strans .3 Strans .4 S150.2 S228.6 S229.7 LLC142.8 LLC147.9 S120.7 SG117 5.22 5.40 5.41 4.95 5.47 6.05 5.34 7. 06 5.39 5.22 SG428 4.954.44.49 4.20 5.01 3.89 5,07 6,31 5,59 4, 95 SG459 6,39 6, 14 5,46 4, 98 7, 14 5, 72 6,23 9,01 8, 56 6,39 SG461 5, 73 6, 16 6.23 6.38 6, 72 6.01 6.39 7, 02 7, 05 5.73 SG493 6, 78 7, 05 5, 03 5, 15 6, 95 6.37 6.22 7.87. 8.07 6, 78 SG507 7, 83 7, 62 5, 83 5.78 8, 17 7, 26 7.85 9, 74 8, 82 7, 83 SG508 6, 42 6.46 6, 90 4, 56 6, 62 6.70 6.71 8, 19 8, 17 6, 42 Xi 107.44 -99, 01 -48.21 -172, 85 -40, 21 -93, 50 -56.20 85, 27 64, 43 107, 44 Pi 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 fifteen Imni LLC148b.lO LLC148c.ll LLC111.12 LLC163.13 LLC108.15 LLC160.1 LLC160.2 LLC187.5 SG117 6, 98 6.83 6, 54 6, 16 6.87 7, 74 7, 71 7.58 S6428 6, 64 6, 63 6, 17 5, 61 6.56 5, 92 5.69 7.24 SG459 9, 13 9.29 8, 44 8.33 8.55 8.29 8, 25 9.01 SG461 7, 16 7.48 6, 67 6, 61 6.86 7.21 7, 25 7.35 SG493 7, 70 7, 89 8, 05 7, 92 7, 67 7, 66 7, 58 8, 05 SG507 9, 90 10, 02 9, 16 9, 16 9.66 8.88 8, 93 9.99 SG508 8.27 8.45 7, 92 7.86 7.89 7.57 57, 51 8.30 Xi 102, 70 125, 01 39.43 28, 30 58.29 51, 63 48, 76 109.23 Pi 1, 00 1, 00 1,00 1, 00 1,00 1,00 1, 00 1, 00 I'm LLC197 LLC198 LLC199 LLC200 LLC201 LLC20 LLC20 LLC21 nor .14 .15 .16 .17 .18 _LE 8.1 0.2 SG1 17 6, 66 6, 13 7, 14 7, 58 7, 97 6, 67 7,40 7,11 SG4 28 5, 98 5, 10 6, 71 6, 72 7, 54 5, 97 6, 77 6.71 SG4 59 8.35 7, 97 8, 34 8.99 9.30 8, 26 8, 34 8, 19 SG4 61 6.76 6.36 7, 02 7.26 7.35 6, 83 6, 99 6.79 SG4 93 7.80 7, 27 7, 96 8.09 8.39 7, 53 7, 44 7.48 SG5 07 9.38 8, 71 9, 58 9, 87 10, 23 9, 04 8, 65 9.26 SG5 8.00 7.48 7, 72 8, 64 8, 76 7.17 7, 09 7, 61 08 116, 7 134, 6 Xi 48.40 0.40 48.33 5 9 14, 31 29, 64 39.38 Pi 1.00 0.60 1.00 1.00 1.00 1.00 1.00 1, 00 Imni LLC225.6 LLC236.7 LLC240.8 LLC184b.9 LLC184C.10 LLC208.1 LLC213.5 LLC214.6 SG117 7.35 6, 54 6.80 7, 12 7, 06 7, 24 6, 97 7, 06 SG428 6.46 6.43 6.11 6, 97 6.36 6.34 5.80 6.45 SG459 8.20 7, 60 8, 24 8.37 8.37 8, 07 8, 17 8.35 SG461 6, 90 6, 85 6, 58 7, 05 6, 73 6, 53 6.43 6, 79 SG493 7.28 6, 90 6.77 7.40 7, 17 7.59 7.47 7.63 SG507 8.45 8, 55 8, 85 9, 22 8, 88 8, 08 8.61 9, 13 SG508 7, 13 7.29 6, 93 8.21 8, 01 7, 14 7, 77 7, 72 Xi 25.36 22.47 7, 52 88, 13 65.29 0.80 26, 04 44, 00 Pi 1, 00 1,00 1, 00 1, 00 1, 00 0,69 1, 00 1, 00 AXIS PLO 5: RESULTS OBTAINED WITH SAMPLES LLC "STABLE" 'AGAINST SAMPLES LLC "PROGRESSIVE" Samples were considered "LLC-stable type" (E) those samples from patients who have been in stable LLC for more than 5 years and samples "LLC- Progressive type "(P)" progressive type "samples from patients classified as stable at the time of diagnosis and whose disease has progressed in less than a year. In total, 6 E samples and 6 P samples were analyzed. The 12 samples were collected at the time of diagnosis, there were no clinical differences between them, but after one year, 6 of those patients had progressed. The 12 hybridizations have passed the quality criteria detailed above. Stable Samples: E142R, E148, E156, E163, E164, E193 Progressive Samples: Pili, P105, P177, P158, P157 and P197. All data analysis was performed in R version 1.9.1.

Data normalization. In this case, and to avoid that the significant genes obtained are due to a true difference between samples and not to a normalization effect, the data were normalized in two different ways ("variance stabilization normalization" (vsn) and by robust quantiles) and with each of the normalizations, the same statistical analysis was performed. Statistical analysis with data normalized by "variance stabilization normalization". The list of statistically significant genes was obtained from a Welch test with the mt.maxT function of the multtest package of R, with a value of p < 0.05 unadjusted, that is, without making any control over the false positives, and produced a list of 29 genes with statistically significant differences between the LLC group-stable type and LLC-progressive type. The statistically significant oligonucleotides obtained were: SG26, SG31, SG70, SG98, SG177, SG194, SG195, SG208, SG213, SG216, SG272, SG293, SG301, SG309, SG321, SG333, SG343, SG352, SG357, SG366, SG368, SG405, SG426, SG439, SG447, SG452, SG521, SG555, SG556. The grouping of the samples was then carried out, which was done with the hclust function of the stats package of R applying Pearson correlations. The tree obtained is shown in part A of Figure 4. The hierarchical grouping of the 12 samples based on the expression of the 29 statistically significant genes obtained grouped the samples correctly: the tree contains two large branches, of which the branch on the right contains the 6 stable samples and the branch on the left contains the 6 progressive samples. Statistical analysis with data normalized by robust quantiles The list of statistically significant genes was obtained from a Welch test with the mt.maxT function of the multtest package of R with the values of p unadjusted, that is, without exerting any control over the false positive rate, with a p <value; 0.05, and produced a list of 19 genes with statistically significant differences between the group LLC-stable type and LLC-progressive type: SG26, SG31, SG177, SG194, SG195, SG197, SG213, SG216, SG293, SG301, SG309 , SG333, SG343, SG357, SG366, SG439, SG452, SG555, SG556. The supervised grouping of the 12 samples based on the expression of the 19 statistically significant genes obtained gave rise to the tree that appears in part B of Figure 4, in which the samples are also grouped correctly. 18 oligonucleotides common to both lists of statistically significant genes were selected and the average intensity of each of them was calculated in the group of stable samples and in the group of progressive samples, as well as the average intensity variation between the group of stable and progressive. The values obtained are shown in Table 14. Table 14.- Values corresponding to the intensity of 18 Significant oligonucleotides to distinguish between LLC- stable and LLC-progressive Group LLC Group LLC Change Meaning stable progressive establishment (p Probe Media Media / data Intensi Intensid Progress vsn) SD ad SD ivo 1.7 SG177 0.001 14 1 21 4, 84 0.7 2,3 SG366 0.001 18 3 14 1, 80 1.3 2.7 SG309 0, 004 20 6 15 3, 58 1.4 19, 13.2 SG26 0, 005 97 20 70, 4 1.4 SG452 0, 010 16 2.1 12 3, 08 1.3 14, SG216 0.012 46 64 31 7.13 1.5 7.2 16.2 SG333 0.013 36 8 5 0.7 6.5 38.6 SG357 0.014 134 0 175 7 0.8 5.5 17, 2 SG213 0.014 32, 10.5 SG31 0.014 69 50 30 1.8 5.0 SG301 0.014 21 16 3.10 1.4 9.5 SG194 0.019 37 2 50 9 , 95 0.7 2, 0 SG456 0.022 11 6 14 2.08 0.8 1.8 SG293 0.029 17 8 21 3.72 0.8 7.4 SG343 0.033 27 21 1.61 1.3 SG439 0.038 18 2.0 20 1.74 0.9 0 3.5 SG195 O, 041 21 6 25, 4, 60 0.8 23, 20.6 SG555 0, 049 163 55 137 9 1.2 To validate the results obtained with the microarray, we selected 5 of the common statistically significant probes obtained when comparing expression data of stable CLL subjects versus progressive CLL subjects and we proceeded to study the expression with RT-PCR of the genes represented by those probes. The criteria used for the selection of the 5 probes were: intensity of hybridization, change of intensity between groups of stable and progressive and value of statistical significance. In this way, 5 probes representing the genes PSMB4, CD23A, LCP1, ABCC5 and POU2F2 were selected. The expression of these 5 genes was determined in 11 of the 12 CL type samples, since total RA of sample 105 was not available. With the expression values of the genes in each sample, the rate of change between the samples was determined. group of stable and progressive and the significance value of that variation and compared with the results obtained with the microarrays.

The technique used for the validation was RT-PCR or PCR in real time using a LightCycler. This technique is the technique of choice to validate chip data and like that the microarrays, measure level of mR A. Primers were designed for each of the 5 genes whose Representative oligonucleotide was selected. The details of them are shown below in Table 15. Table 15.- Primers and amplification products of the genes selected for validation by RT-PCR Size Primer sequences (5'- SEQ ID product Gen Tm 3 ') NO: amplific adored Direct: PSMB4 F SEQ ID 0 TTCTGGGAGATGGACACAGCTATA NO: 598 81 ° PSMB4 95pb Reverse: PS B4_R SEQ ID C CCACAAAGGGTTCATCTTCGA NO: 599 Size Primer sequences (5'- SEQ ID product Gen Tm 3 ') NO: amplific adored Direct: CD23A_F SEQ ID TGCCCTGAAAAGTGGATCAAT NO: 600 82 ° CD23A 97pb Reverse: CD23A_R SEQ ID C CCATGTCGTCACAGGCATACC NO: 601 Direct: LCP1_F SEQ ID CCAGGTACCCTTCTCGCTTTT NO: 602 77 ° LCP1 126pb Reverse: LCP1_R SEQ ID C CTCCTGGCCCTCATCTTGAA NO: 603 Direct: ABCC5_F SEQ ID CCCTCAAAGTCTGCAACTTTAAGC NO: 604 82 ° ABCC5 119pb Reverse: ABCC5_R SEQ ID C ACACACCAAACCACACAGCAA NO: 605 Size Primer sequences (5'- SEQ ID product Gen Tm 3 ') NO: amplific adored Direct: POU2F2_F SEQ ID GAGGACCAGCATCGAGACAAA NO: P0U2F 606 82 ° 136pb 2 Reverse: POU2F2 R SEQ ID C AACCAGACGCGGATCACTTC NO: 607 Figure 5 shows the distribution of the data of expression obtained by RT-PCR (part graphs) left) and using the microarray (part graphs) right) . Part A corresponds to the PSMB4 gene, part B to CD23A gene and part C to the POU2F2 gene.

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Westbrook CA. The molecular basis of neoplasia. In Hoffman R Hematology Basic Principles and practice. 4 ed. Churchill Livingstone New York 2005; 941-945 Zhan F, Hardin J, Kordsmeier B, et al: Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells. Blood 99: 1745, 2002 BRIEF DESCRIPTION OF THE FIGURES Figure 1 shows the grouping of samples of U937 cells against Jurkat cells according to differences in gene expression between the samples. Part A corresponds to the unsupervised grouping; Part B corresponds to the supervised grouping. Figure 2 shows the clustering of samples from healthy subjects against U937 cells and Jurkat cells as a function of differences in gene expression between samples. Part A corresponds to the unsupervised grouping; Part B corresponds to the supervised grouping. Figure 3 shows the grouping of samples from patients with chronic lymphatic leukemia against U937 cells and Jurkat cells based on differences in gene expression between samples. Part A corresponds to the unsupervised grouping; Part B corresponds to the supervised grouping.

Figure 4 shows the grouping of samples from patients with "stable" chronic lymphatic leukemia versus samples from patients with "progressive" chronic lymphatic leukemia depending on differences in gene expression. Part A corresponds to the grouping according to the genes identified as significant after normalization with "vsn" and utilization of the mt.maxT function of R; Part B corresponds to the grouping according to the genes identified as significant after normalization by robust quantiles and use of the mt.maxT function of R.

Figure 5 shows the distribution of the expression data obtained by RT-PCR (left side graphs) and from the intensity values obtained from the microarray (right side graphs) for the PSMB4 genes (part A: upper graphs) ), CD23A (part B: intermediate charts) and POU2F2 (part C: lower charts) in samples from patients with "stable" chronic lymphatic leukemia (bars marked with "E") and in samples from patients with "progressive" chronic lymphocytic leukemia (bars marked with "P").

Claims (61)

1. CLAIMS 1. A composition comprising at least one oligonucleotide from the group consisting of: SG1, SG2, SG3, SG4, SG5, SG6, SG7, SG8, SG9, SG10, SG11 SG12, SG13, SG14, SG15, SG16, SG17, SG18 , SG19, SG20, SG21 SG22, SG23, SG24, SG25, SG26, SG27, SG28, SG29, SG30, SG31 SG32, SG33, SG34, SG35, SG36, SG37, SG38, SG39, SG40, SG41 SG42, SG43, SG44, SG45, SG46, SG47, SG48, SG49, SG50, SG51 SG52, SG53, SG54, SG55, SG56, SG57, SG58, SG59, SG60, SG61 SG62, SG63, SG64, SG65, SG66, SG67, SG68, SG69, SG70, SG71 SG72, SG73, SG74, SG75, SG76, SG77, SG78, SG79, SG80, SG81 SG82, SG83, SG84, SG85, SG86, SG87, SG88, SG89, SG90, SG91 SG92, SG93, SG94, SG95, SG96, SG97 , SG98, SG99, SG100, SG101 SG102, SG103, SG104, SG105, SG106, SG107, SG108, SG109 SG110, SG111, SG112, SG113, SG114, SG115, SG116, SG117 SG118, SG119, SG120, SG121, SG122, SG123, SG124, SG125 SG126, SG127, SG128, SG129, SG130, SG131, SG132, SG133 SG134, SG135, SG136, SG137, SG138, SG139, SG140, SG141 SG142, SG143, SG144, SG145, S G146, SG147, SG148, SG149 SG150, SG151, SG152, SG153, SG154, SG155, SG156, SG157 SG158, SG159, SG160, SG161, SG162, SG163, SG164, SG165 SG166, SG167, SG168, SG169, SG170, SG171, SG172 , SG173 SG174, SG175, SG176, SG177, SG178, SG179, SG180, SG181 SG182, SG183, SG184, SG185 SG186 SG187, SG188, SG189, SG190, SG191, SG192, SG193 SG194 SG195, SG196, SG197, SG198, SG199, SG200, SG201 SG202 SG203, SG204, SG205, SG206, SG207, SG208, SG209 SG210 SG211, SG212, SG213, SG214, SG215, SG216, SG217 SG218 SG219, SG220, SG221, SG222, SG223, SG224, SG225 SG226 SG227, SG228, SG229, SG230, SG231, SG232, SG233 SG234 SG235, SG236, SG237, SG238, SG239, SG240, SG241 SG242 SG243, SG244, SG245, SG246, SG247, SG248, SG249 SG250 SG251, SG252, SG253, SG254, SG255, SG256, SG257 SG258 SG259, SG260, SG261, SG262, SG263, SG264, SG265 SG266 SG267, SG268, SG269, SG270, SG271, SG272, SG273 SG274 SG275, SG276, SG277, SG278, SG279, SG280, SG281 SG282 SG283, SG284, SG285, SG286, SG287, SG288, SG289 SG290 SG291, SG292, SG293, SG294, SG295, SG296, SG297 SG298 SG299, SG300, SG301, SG302, SG303, SG304, SG305 SG306 SG307, SG308, SG309, SG310, SG311, SG312, SG313 SG314 SG315, SG316, SG317, SG318, SG319, SG320, SG321 SG322 SG323, SG324, SG325, SG326, SG327, SG328, SG329 SG330 SG331, SG332, SG333, SG334, SG335, SG336, SG337 SG338 SG339, SG340, SG341, SG342, SG343, SG344, SG345 SG346 SG347, SG348, SG349, SG350, SG351, SG352, SG353 SG354 SG355, SG356, SG357, SG358, SG359, SG360, SG361 SG362 SG363, SG364, SG365, SG366, SG367, SG368, SG369, SG370, SG371 SG372, SG373, SG374, SG375, SG376, SG377, SG378 SG379 SG380, SG381, SG382, SG383, SG384, SG385, SG386 SG387 SG388, SG389, SG390, SG391, SG392, SG393, SG394 SG395 SG396, SG397, SG398, SG399, SG400, SG401, SG402 SG403 SG404, SG405, SG406, SG407, SG408, SG409, SG410 SG411 SG412, SG413, SG414, SG415, SG416, SG417, SG418 SG419 SG420, SG421, SG422, SG423, SG424, SG425, SG426 SG427 SG428, SG429, SG430, SG431, SG432, SG433, SG43 SG435 SG436, SG437, SG438, SG439, SG440, SG441, SG442 SG443 SG444, SG445, SG446, SG447, SG448, SG449, SG450 SG451 SG452, SG453, SG454, SG455, SG456, SG457, SG458 SG459 SG460, SG461, SG462, SG465, SG468, SG469, SG470 SG471 SG472, SG473, SG474, SG475, SG476, SG477, SG478 SG479 SG480, SG481, SG482, SG483, SG484, SG485, SG486 SG487 SG488, SG489, SG490, SG491, SG492, SG493, SG494 SG495 SG496, SG497, SG498, SG499, SG500, SG501, SG502 SG503 SG504, SG505, SG506, SG507, SG508, SG509, SG510 SG511 SG512, SG513, SG514, SG515, SG516, SG517, SG518 SG519 SG520, SG521, SG522, SG523, SG524, SG525, SG526 SG527 SG428, SG529, SG530, SG531, SG532, SG533, SG534 SG535 SG536, SG537, SG538, SG539, SG540, SG541, SG542 SG543 SG544, SG545, SG546, SG547, SG548, SG549, SG550 SG551 SG552, SG553, SG554, SG555, SG556, SG557, SG558, SG559, SG560, SG561, SG562, SG563, or combinations thereof, to be used as a probe in determining the level of expression of a gene that possesses a sequence complementary to said oligonucleotide by means of the assessment of the level of mRNA corresponding to that gene, of application in the in vitro diagnosis of neoplasms originated from hematopoietic cells and / or in the in vitro prognosis of the evolution of said disease.
2. 2. A composition according to claim 1, comprising at least oligonucleotides SG117, SG428, SG459, SG507, SG508.
3. 3. A composition according to claim 2, further comprising at least oligonucleotides SG461 and SG493.
4. 4. A composition according to claim 2, further comprising at least oligonucleotide SG237.
5. 5. A composition according to claim 4, further comprising at least one oligonucleotide selected from the group of SG2, SG4, SG8, SG10, SG13, SG15, SG16, SG19, SG20, SG23, SG26, SG28, SG31, SG34, SG36, SG39, SG48, SG58, SG60, SG65, SG76, SG77, SG84, SG89, SG94, SG9, SG97, SG99, SG102, SG106, SG107, SG463, SG115, SG116, SG120, SG129, SG134, SG135, SG138, SG139, SG141, SG145, SG158, SG161, SG163, SG176, SG178, SG185, SG207, SG208, SG210, SG217, SG227, SG231, SG237, SG264, SG272, SG277, SG281, SG283, SG286, SG294, SG298, SG299, SG307, SG308, SG319, SG328, SG330, SG333, SG336, SG342, SG344, SG345, SG347, SG361, SG384, SG389, SG395, SG404, SG407, SG416, SG423, SG430, SG432, SG434, SG444, SG446, SG453, SG458, SG464, SG465, SG466, SG467, SG471, SG473, SG474, SG475, SG481, SG485, SG487, SG491, SG498, SG511, SG517, SG518, SG522, SG526, SG530, SG533, SG538, SG541, SG554, SG558, SG561 or combinations thereof.
6. 6. A composition according to any of claims 2 to 5, for use in the in vitro diagnosis of chronic lymphatic leukemia.
7. 7. A composition according to claim 1, comprising at least the oligonucleotides SG26, SG216, SG366.
8. 8. A composition according to claim 7, further comprising at least one oligonucleotide selected from the group of SG31, SG177, SG194, SG195, SG197, SG213, SG293, SG301, SG309, SG333, SG343, SG357, SG439, SG452, SG555, SG556.
9. 9. A composition according to any of claims 7 or 8, to be used in the in vitro prognosis of the future evolution of the disease in a patient suffering from chronic lymphatic leukemia.
10. 10. A composition according to claim 1, comprising all of the nucleotides of the group consisting of: SG1, SG2, SG3, SG4, SG5, SG6, SG7, SG8, SG9, SG10, SG11, SG12, SG13, SG14, SG15 , SG16, SG17, SG18, SG19, SG20, SG21, SG22, SG23, SG24, SG25, SG26, SG27, SG28, SG29, SG30, SG31, SG32, SG33, SG34, SG35, SG36, SG37, SG38, SG39, SG40 , SG41, SG42, SG43, SG44, SG45, SG46, SG47, SG48, SG49, SG50, SG51, SG52, SG53, SG54, SG55, SG56, SG57, SG58, SG59, SG60, SG61, SG62, SG63, SG64, SG65 , SG66, SG67, SG68, SG69, SG70, SG71, SG72, SG73, SG74, SG75, SG76, SG77, SG78, SG79, SG80, SG81, SG82, SG83, SG84, SG85, SG86, SG87, SG88, SG89, SG90 , SG91, SG92, SG93, SG94, SG95, SG96, SG97, SG98, SG99, SG100, SG101, SG102, SG103, SG104, SG105, SG106, SG107, SG108, SG109, SG110, SG111, SG112, SG113, SG114, SG115 , SG116, SG117, SG118, SG119, SG120, SG121, SG122, SGI23, SG124, SG125, SG126, SG127, SG128, SG129, SG130, SG131, SG132, SG133, SG134, SG135, SG136, SG137, SG138, SG139 , SG140, SG141, SG142, SG143, SG144, SG145, SG146, SG147, SG148, SG149, SG150, SG151, SG152 SG153, SG154, SG155, SG156, SG157, SG158, SG159, SG160 SG161, SG162, SG163, SG164, SG165, SG166, SG167, SG168 SG169, SG170, SG171, SG172, SG173, SG174, SG175, SG176 SG177, SG178, SG179, SG180, SG181, SG182, SG183, SG184 SG185, SG186, SG187, SG188, SG189, SG190, SG191, SG192 SG193, SG194, SG195, SG196, SG197, SG198, SG199, SG200 SG201, SG202, SG203, SG204, SG205, SG206, SG207, SG208 SG209, SG210, SG211, SG212, SG213, SG214, SG215, SG216 SG217, SG218, SG219, SG220, SG221, SG222, SG223, SG224 SG225, SG226, SG227, SG228, SG229, SG230, SG231, SG232 SG233, SG234, SG235, SG236, SG237, SG238, SG239, SG240 SG241, SG242, SG243, SG244, SG245, SG246, SG247, SG248 SG249, SG250, SG251, SG252, SG253, SG254, SG255, SG256 SG257, SG258, SG259, SG260, SG261, SG262, SG263, SG264 SG265, SG266, SG267, SG268, SG269, SG270, SG271, SG272 SG273, SG274, SG275, SG276, SG277, SG278, SG279, SG280 SG281, SG282, SG283, SG284, SG285, SG286, SG287, SG288 SG289, SG290, SG291, SG292, SG293, SG294, SG295, SG296 SG297, SG298, SG299, SG300, SG301, SG302, SG303, SG304 SG305, SG306, SG307, SG308, SG309, SG310, SG311, SG312 SG313, SG314, SG315, SG316, SG317, SG318, SG319, SG320 SG321, SG322, SG323, SG324, SG325, SG326, SG327, SG328 SG329, SG330, SG331, SG332, SG333, SG334, SG335, SG336, SG337, SG338 SG339, SG340, SG341, SG342, SG343, SG344, SG345, SG346 SG347, SG348, SG349, SG350, SG351, SG352, SG353, SG354 SG355, SG356, SG357, SG358, SG359, SG360, SG361, SG362 SG363, SG364, SG365, SG366, SG367, SG368, SG369, SG370 SG371, SG372, SG373, SG374, SG375, SG376, SG377, SG378 SG379, SG380, SG381, SG382, SG383, SG384, SG385, SG386 SG387, SG388, SG389, SG390, SG391, SG392, SG393, SG394 SG395, SG396, SG397, SG398, SG399, SG400, SG401, SG402 SG403, SG404, SG405, SG406, SG407, SG408, SG409, SG410 SG411, SG412, SG413, SG414, SG415, SG416, SG417, SG418 SG419, SG420, SG421, SG422, SG423, SG424, SG425, SG426 SG427, SG428, SG429, SG430, SG431, SG432, SG433, SG434 SG435, SG436, SG437, SG438, SG439, SG440, SG441, SG442 SG443, SG444, SG445, SG446, SG447, SG448, SG449, SG450 SG451, SG452, SG453, SG454, SG455, SG456, SG457, SG458 SG459, SG460, SG461, SG462, SG465, SG468, SG469, SG470 SG471, SG472, SG473, SG474, SG475, SG476, SG477, SG478 SG479, SG480, SG481, SG482, SG483, SG484, SG485, SG486 SG487, SG488, SG489, SG490, SG491, SG492, SG493, SG494 SG495, SG496, SG497, SG498, SG499, SG500, SG501, SG502 SG503, SG504, SG505, SG506, SG507, SG508, SG509, SG510 SG511, SG512, SG513, SG514, SG515, SG516, SG517, SG518 SG519, SG520, SG521, SG522, SG523, SG524, SG525, SG526, SG527, SG428, SG529, SG530, SG531, SG532, SG533, SG534, SG535, SG536, SG537, SG538, SG539, SG540, SG541, SG542, SG543, SG544, SG545, SG546, SG547, SG548, SG549, SG550, SG551, SG552, SG553, SG554, SG555, SG556, SG557, SG558, SG559, SG560, SG561, SG562, SG563. A composition according to any of claims 1 to 10, characterized in that it additionally comprises at least one oligonucleotide selected from the group consisting of SG463, SG464, SG466, SG467, SSPC1, SSPC2, SSPC3, SSPC4, SSPC5, SSPC6, SSPC7, SCN1, SCN2 , SCN3, SCN5, SCN6, SCN7, SCN8, SCN10, SCN
11. 11, SCN12, SCN13, SCI, SC2, SC3, SC4, SC5, SC6 and SC7.
12. 12. A composition according to claim 11, comprising all the oligonucleotides of the group consisting of SG463, SG464, SG466, SG467, SSPC1, SSPC2, SSPC3, SSPC4, SSPC5, SSPC6, SSPC7, SCN1, SCN2, SCN3, SCN5, SCN6, SCN7, SCN8, SCN10, SCN11, SCN12, SCN13, SCI, SC2, SC3, SC4, SC5, SC6 and SC7.
13. 13. A composition according to any of claims 1 to 10, wherein the oligonucleotides are arranged on a solid support.
14. 14. A composition according to claim 13, wherein the oligonucleotides are arranged in an orderly manner on a solid support which is a glass similar to a slide to which the oligonucleotides are bound by covalent bonds, forming a microarray.
15. 15. A composition in the form of a microarray according to claim 14, comprising all of the oligonucleotides of the group consisting of SG1, SG2,, SG3, SG4, SG5, SG6,, SG7, SG8, SG9, SG10, SG11, SG12, SG13, SG14, SG15, SG16, SG17, SG18, SG19, SG20, SG21, SG22, SG23, SG24, SG25, SG26, SG27, SG28, SG29, SG30, SG31, SG32, SG33, SG34, SG35, SG36, SG37, SG38, SG39, SG40, SG41, SG42, SG43, SG44, SG45, SG46, SG47, SG48, SG49, SG50, SG51, SG52, SG53, SG54, SG55, SG56, SG57, SG58, SG59, SG60, SG61, SG62, SG63, SG64, SG65, SG66, SG67, SG68, SG69, SG70, SG71, SG72, SG73, SG74, SG75, SG76, SG77, SG78, SG79, SG80, SG81, SG82, SG83, SG84, SG85, SG86, SG87, SG88, SG89, SG90, SG91, SG92, SG93, SG94, SG95, SG96, SG97, SG98, SG99, SG100, SG101, SG102, SG103, SG104, SG105, SG106, SG107, SG108, SG109, SG110, SG111, SG112, SG113, SG114, SG115, SG116, SG117, SG118, SG119, SG120, SG121, SG122, SG123, SG124, SG125, SG126, SG127, SG128, SG129, SG130, SG131, SG132, SG133, SG134, SG135, SG136, SG137, SG138, SG139, SG140, SG141, SG142, SG143 SG144, SG145, SG146, SG147, SG148, SG149, SG150, SG151 SG152, SG153, SG154, SG155, SG156, SG157, SG158, SG159 SG160, SG161, SG162, SG163, SG164, SG165, SG166, SG167 SG168, SG169, SG170, SG171, SG172, SG173, SG174, SG175 SG176, SG177, SG178, SG179, SG180, SG181, SG182, SG183 SG184, SG185, SG186, SG187, SG188, SG189, SG190, SG191 SG192, SG193 SG194, SG195, SG196, SG197, SG198, SG199 SG200, SG201, SG202, SG203, SG204, SG205, SG206, SG207 SG208, SG209, SG210, SG211, SG212, SG213, SG214, SG215 SG216, SG217, SG218, SG219, SG220, SG221, SG222, SG223 SG224, SG225, SG226, SG227, SG228, SG229, SG230, SG231 SG232, SG233, SG234, SG235, SG236, SG237, SG238, SG239 SG240, SG241, SG242, SG243, SG244, SG245, SG246, SG247 SG248, SG249, SG250, SG251, SG252, SG253, SG254, SG255 SG256, SG257, SG258, SG259, SG260, SG261, SG262, SG263 SG264, SG265 SG266, SG267, SG268, SG269, SG270, SG271 SG272, SG273 SG274, SG275, SG276, SG277, SG278, SG279 SG280, SG281 SG282, SG283, SG284, SG285, SG286, SG287 SG288, SG289, SG290, SG291, SG292, SG293, SG294, SG295 SG296, SG297, SG298, SG299, SG300, SG301, SG302, SG303 SG304, SG305, SG306, SG307, SG308, SG309, SG310, SG311 SG312, SG313, SG314, SG315, SG316, SG317, SG318, SG319 SG320, SG321, SG322, SG323, SG324, SG325, SG326, SG327, SG328, SG329 SG330, SG331, SG332, SG333, SG334, SG33 5, SG336, SG337 SG338, SG339, SG340, SG341, SG342, SG34 3, SG344, SG345 SG346, SG347, SG348, SG349, SG350, SG35 1, SG352, SG353 SG354, SG355, SG356, SG357, SG358, SG35 9, SG360, SG361 SG362, SG363, SG364, SG365, SG366, SG36 7, SG368, SG369 SG370, SG371, SG372, SG373, SG374, SG37 5, SG376, SG377 SG378, SG379, SG380, SG381, SG382, SG38 3, SG384, SG385 SG386, SG387, SG388, SG389, SG390, SG39 1, SG392, SG393 SG394, SG395, SG396, SG397, SG398, SG39 9, SG400, SG401 SG402, SG403, SG404, SG405, SG406, SG40 7, SG408, SG409 SG410, SG411, SG412, SG413, SG414, SG41 5, SG416, SG417 SG418, SG419, SG420, SG421, SG422, SG42 3, SG424, SG425 SG426, SG427, SG428, SG429, SG430, SG43 1, SG432, SG433 SG434, SG435, SG436, SG437, SG438, SG43 9, SG440, SG441 SG442, SG443, SG444, SG445, SG446, SG44 7, SG448, SG449 SG450, SG451, SG452, SG453, SG454, SG45 5, SG456, SG457 SG458, SG459, SG460, SG461, SG462, SG46 5, SG468, SG469 SG470, SG471, SG472, SG473, SG474, SG47 5, SG476, SG477 SG478, SG479, SG480, SG481, SG482, SG48 3, SG484, SG485 SG486, SG487, SG488, SG489, SG490, SG49 1, SG492, SG493 SG494, SG495, SG496, SG497, SG498, SG49 9, SG500, SG501 SG502, SG503, SG504, SG505, SG506, SG50 7, SG508, SG509 SG510, SG511, SG512, SG513, SG514, SG515, SG516, SG517, SG518, SG519, SG520, SG521, SG522, SG523, SG524, SG525, SG526, SG527, SG428, SG529, SG530, SG531, SG532, SG533, SG534, SG535, SG536, SG537, SG538, SG539, SG540, SG541, SG542, SG543, SG544, SG545, SG546, SG547, SG548, SG549, SG550, SG551, SG552, SG553, SG554, SG555, SG556, SG557, SG558, SG559, SG560, SG561, SG562, SG563.
16. 16 A composition in the form of a microarray according to claim 15, further comprising at least one pair of oligonucleotides selected from that composed of oligonucleotides SG463 and SG464 and composed of oligonucleotides SG466 and SG467, at least one oligonucleotide from the group consisting of SSPC1, SSPC2, SSPC3, SSPC4, SSPC5, SSPC6 and SSPC7, at least one oligonucleotide from the group consisting of SCN2, SCN3, SCN6, SCN8, SCN11, SCN12 and SCN13 and at least one oligonucleotide from the group consisting of SCI, SC2, SC3, SC4, SC5, SC6, SC7, SCN1, SCN5, SCN7 and SCN10.
17. 17. A composition in the form of a microarray according to claim 16, comprising all of the oligonucleotides of the group consisting of SG463, SG464, SG466, SG467, SSPC1, SSPC2, SSPC3, SSPC4, SSPC5, SSPC6, SSPC7, SCN2, SCN3, SCN6, SCN8, SCN11, SCN12, SCN13, SCI, SC2, SC3, SC4, SC5, SC6, SC7, SCN1, SCN5, SCN7 and SCN10.
18. 18. A composition in the form of a microarray according to claim 17, further comprising points lacking oligonucleotides in which the solvent in which the oligonucleotides were deposited on said glass is attached to the glass.
19. 19. A composition in the form of a microarray according to claim 18, comprising at least twelve copies of each of the different oligonucleotides present therein, as well as at least twelve spots lacking oligonucleotides in which the solvent in the glass is bound to the glass. that the oligonucleotides were found when deposited on said glass.
20. 20. A composition in the form of a microarray according to any one of claims 18 to 19, wherein at the points lacking oligonucleotides, the solvent DMSO is attached to the glass.
21. 21. A composition in the form of a microarray according to any of claims 15 to 20 to be used in the in vitro diagnosis of chronic lymphatic leukemia and / or for the in vitro prognosis of the evolution of said disease.
22. 22. A device for the in vitro diagnosis of a neoplasm originated from hematopoietic cells and / or for the in vitro prognosis of the evolution thereof, comprising a composition according to any one of claims 1 to 20.
23. 23. A device for the in vitro diagnosis of a neoplasm originated from hematopoietic cells and / or for the in vitro prognosis of the evolution of the same according to claim 22, comprising a composition in the form of a microarray of any of claims 14 to 20 .
24. 24. A device for the in vitro diagnosis of a neoplasm originated from hematopoietic cells and / or for the in vitro prognosis of the evolution of the same according to claim 22, comprising a composition in the form of a microarray of any of claims 19 or twenty.
25. 25. A device for the in vitro diagnosis of a neoplasm originated from hematopoietic cells and / or for the in vitro prognosis of the evolution thereof according to any of claims 23 or 24, in which the neoplasm that is diagnosed or whose evolution It is predicted to be chronic lymphatic leukemia.
26. 26. A method for diagnosing in vitro a neoplasm originated from hematopoietic cells and / or predicting in vitro the evolution thereof which comprises the in vitro detection from a biological sample and the statistical analysis of the expression level of at least a significant gene for classifying the sample as associated or not to said neoplasia, gene that is selected from the group consisting of GABARAP, NPM3, ABCB1, ABCB4, ABCC3, ABCC5, ABCC6, ABHD1, ABL1, ACTN1, AFlq, AKR1A1, ALDH1A1, ALK , ANK2, ANPEP, A XA6, ANXA7, APAF1, APEX, ARHGEF2, ARS2, ASNS, ATIC, ATM, ATP50, BAX, BCL10, BCL2, BCL2A1, BCL2L1, BCL2LAA, BCL3, BCL6, BCL7A, BCL7b, BCR, BECN1, BIK, BIRC3, BIRC5, BLMH, BLR1, BLVRB, BMI1, BMP6, BRMS1, BST2, BTG1, BUB1, C21orf33, C5orfl3, CA12, CALD1, CA P2, CASC3, CASP1, CASP3, CASP4, CASP5, CASP6, CASP7, CASP8 , CASP9, CAST, CATSD, CBFA2T1, CBFB, CCNA1, CCNB1, CCND1, CCND2, CCND3, CCNE1, CCR6, CCR7, CCT6A, CD14, CD19, CD2, CD 22, CD24, CD28, CD33, CD34, CD34, CD38, CD38, CD4E, CD4, CD44, CD44, CD48, CD48, CD48, CD48, CD8, CD8, CD8, CD8, CD8, CD8, CD8, CD8, CD8, CD8, CDC25A, CDC25B, CDK2, CDK4, CDK5R1, CDKN1A, CDK1B, CDKN1C, CDKN2A, CDKN2B, CDK2C, CDKN3, CDW52, CEBPA, CEBPB, CEBPD, CFL1, CKMT1, CKS2, CML66, COL3A1, COL4A6, CR2, CREB1, CREBBP, CRYAB, CSF2, CSF3, CSRP2, CTGF, CTSB, CUZD1, CXADR, CXCL9, CXCR3, CXCR4, CYC1, CYP1A1, CYP2A6, DAD-1, DAPK1, DCK, DDX6, DEK, DHFR, DLAD, DNAJA1, DNMT3B, DNTT, D0K1, DPF2, DPP4, DRG1, DRP2, E2F1, EB-1, EBI2, EDF1, EEF1A1, EEF1B2, EEF1D, EEF1G, EFNB1, EGFR, EGR1, EIF2B2, EIF3S2, EIF4B, EIF4E, EIF5A, ELF1, ELF4, ENPP1, EphA3, EPOR, ERBB2, ERBB4, ERCC1, ERCC2, ERCC3, ERCC5, ERCC6, ETS1, ETS2, ETV6, ETV7, ??? 2, FABP5, FADD, FAIM3, FA 38A, FARP1, FAT, FCER2, FCGR3A, FCGR3B, FGFR1, FGFR3, FGR, FHIT, FKBP9, FLI1, FLJ22169, FLT3, FN1, FNTB, FOS, FUS , G1P2, GABPB2, GATA1, GATA2, GATA3, GCET2, GDI2, GGA3, GJA1, GLUD1, GNL3, GOT1, GRB2, GRIA3, GRK4, GSTP1, GSTT1, GUSB, GZMA, H2AFX, H3F3A, HCK, HELLS, HIF1A, HIST1H2BN , HLA-A, HLA-DPA1, HLA-DQAl, HLA-DRA, HLA-DRB3, HLF, HMMR, HNRPH3, HNRPL, HOXA10, ???? 9, HOXD8, HOXD, HRAS, HSD17B1, HSPB1, IBSP, ICAM1 , ICAM3, ID2, IER3, IFRD1, IGFBP2, IGFBP3, IGFIR, IGLV6-57, IL10, IL15, IL1B, IL2, IL2RA, IL3, IL32, IL3R, IL4R, IL6R, IL6R, IL8, ILF2, IRF1, IRF2, IRF4 , IRF8, ITGA2, ITGA3, ITGA4, ITGA5, ITGA6, ITGAL, ITGAM, ITGAX, ITGB1, ITGB2, JAK1, JAK2, JUNB, KAI1, ???? 0247, ???? 0864, ???, KLF1, KLF13 , KRAS2, KRT18, LADH, LAG3, LASP1, LCK, LCP1, LEPR, LGALS3, LGALS7, LIF, LIMS1, LM02, LOC285148, LRP, LSP1, LYL1, LYN, LYZ, MAFB, MAFK, MAGEA1, MAL, MAP3K12, MAP4K1 , MAPK10, ???, MBP1, MCL1, MCM3, MC 7, MD 2, MEIS1, MEN1, MERTK, ??? 67, MLF1, MLF2, MLL, MLLT10, ???, ??? 2, ??? 7, ??? 8, ??? 9, MNDA, MPL, ???, MRPL37, MS4A1, MTCP1, MUC-1, MX1, ???, MYBL1, MYC, MYOD1, NCALD, NCAM1, NCL, NDP52, NDRG1, NDUFA1, NDUFB, NF1, NFATC1, NFIC, NFKB1, NFKB1A, NINJ1, NPM1, NR3C1, NUMA1, NXF1, ODC1, OGGI, OLIG2, OPRD1, pl4ARF, P55CDC, PABPC1, ??? 5, ??? 6, PAX8, PBX1, PBX3, PCA1, PCD, PCNA, PDCD1, PDGFA, PDGFRB, PDHA1, PGF, PGR C1, PICALM, PLA2G6, PLAU, PLK1, PLP, PLS3, PLZF, PML, PMM1, P0LR2C, POU2F2, PPP1CC, PRAME, PRKCI, PRKCQ, PRKDC, PRL, PRTN3, PSMA5, PSMB4 , PSMC5, PSMD7, PTEN, PTGS1, PTHLH, PTK2, PTK2B, PTN, PTPRCCD, PYGB, RAD51, RAF1, RAG1, RARE, RARB, RB1, RBBP4, RBBP6, RBBP8, RBP4, RET, RGS1, RGS1, RIS1, RORA , RPL17, RPL23A, RPL24, RPL36A, RPL37A, RPL41, RPL41, RPS3, RPS5, RPS9, RUNX1, RXRA, S100A2, S100A8, SDC1, SDHD, SELE, SELL, SEPW1, SERPINA9, SERPINB5, SERPNINA9, SFTPB, SIAT4A, SLC7A5, SNRPB , SOSTDC1, SP1, SPI1, SPN, SPRR1A, SREBF1, SSBP1, STAT1, STAT3, ST AT5B, SUMOl, TACSTD2, TAGLN2, TAL1, TBP, TCEB1, TCF1, TCF3, TCF7, TCL1A, TCRbeta, TEGT, TERF1, TERT, TFCP2, TFRC, THBS1, THPO, TIA-2, TIAM1, TK1, TLX1, TMEM4, TNF, TNFRSF10C, TNFRSF1A, TNFRSF25, TNFRSF5, TNFRSF6, TNFRSF8, TNFSF10, TNFSF5, TNFSF6, TOP2A, TOPORS, TP73, TRA, TRADD, TRAF3, TRAP1, TRIB2, TXNRD1, UBE2C, UHRF1, UVRAG, VCAM1, VEGF, VPREB1 , BSCR20C, NT16, WT1, XBP1, XP06, XRCC3, XRCC5, ZAP70, ZFPL1, ZNF42, ZNFN1A1, ZYX, 18S rRNA, 28S rRNA and whose level of expression is determined by evaluating the concentration of its corresponding mRNA by using of at least one probe having a sequence complementary to a fragment of a strand of said gene, which probe is selected from the group of oligonucleotide composed of: SG1, SG2, SG3, SG4, SG5, SG6, SG7, SG8, SG9, SG10 , SG11 SG12, SG13, SG14, SG15, SG16, SG17, SG18, SG19, SG20, SG21 SG22, SG23, SG24, SG25, SG26, SG27, SG28, SG29, SG30, SG31 SG32, SG33, SG34, SG35, SG36, SG37, SG38, SG39, SG40, SG41 SG42, SG43, SG44, SG45, SG46, SG47, SG48, SG49, SG50, SG51 SG52, SG53, SG54, SG55, SG56, SG57, SG58, SG59, SG60, SG61 SG62, SG63, SG64, SG65, SG66, SG67, SG68, SG69, SG70, SG71 SG72, SG73, SG74, SG75, SG76, SG77, SG78, SG79, SG80, SG81 SG82, SG83, SG84, SG85, SG86, SG87, SG88, SG89, SG90, SG91 SG92, SG93, SG94, SG95, SG96, SG97, SG98, SG99, SG100, SG101 SG102, SG103, SG104, SG105, SG106, SG107, SG108, SG109 SG110, SG111, SG112, SG113, SG114, SG115, SG116, SG117 SG118, SG119, SG120, SG121, SG122, SG123, SG124, SG125 SG126, SG127, SG128, SG129, SG130, SG131, SG132, SG133 SG134, SG135, SG136, SG137, SG138, SG139, SG140, SG141 SG142, SG143, SG144, SG145, SG146, SG147, SG148, SG149 SG150, SG151, SG152, SG153, SG154, SG155, SG156, SG157 SG158, SG159, SG160, SG161, SG162, SG163, SG164, SG165 SG166, SG167, SG168, SG169, SG170, SG171, SG172, SG173 SG174, SG175, SG176, SG177, SG178, SG179, SG180, SG181 SG182, SG183, SG184, SG185, SG186, SG187, SG188, SG189 SG190, SG191 SG192, SG193 SG194, SG195, SG196, SG197, SG198, SG199 SG200, SG201 SG202, SG203, SG204, SG205, SG206, SG207 SG208, SG209 SG210, SG211, SG212, SG213, SG214, SG215 SG216, SG217 SG218, SG219, SG220, SG221, SG222, SG223 SG224, SG225 SG226, SG227, SG228, SG229, SG230, SG231 SG232, SG233 SG234, SG235, SG236, SG237, SG238, SG239 SG240, SG241 SG242, SG243, SG244, SG245, SG246, SG247 SG248, SG249 SG250, SG251, SG252, SG253, SG254, SG255 SG256, SG257 SG258, SG259, SG260, SG261, SG262, SG263 SG264, SG265 SG266, SG267, SG268, SG269, SG270, SG271 SG272, SG273 SG274, SG275, SG276, SG277, SG278, SG279 SG280, SG281 SG282, SG283, SG284, SG285, SG286, SG287 SG288, SG289 SG290, SG291, SG292, SG293, SG294, SG295 SG296, SG297 SG298, SG299, SG300, SG301, SG302, SG303 SG304, SG305 SG306, SG307, SG308, SG309, SG310, SG311 SG312, SG313 SG314, SG315, SG316, SG317, SG318, SG319 SG320, SG321 SG322, SG323, SG324, SG325, SG326, SG327 SG328, SG329 SG330, SG331, SG332, SG333, SG334, SG335 SG336, SG337 SG338, SG339, SG340, SG341, SG342, SG343 SG344, SG345 SG346, SG347, SG348, SG349, SG350, SG351 SG352, SG353 SG354, SG355, SG356, SG357, SG358, SG359 SG360, SG361 SG362, SG363, SG364, SG365, SG366, SG367 SG368, SG369 SG370, SG371, SG372, SG373, SG375 SG375 SG376 SG377 SG377, SG379, SG380, SG381, SG382 SG383 SG384 SG385 SG386, SG387, SG388, SG389, SG390 SG391 SG392 SG393 SG393 SG394, SG395, SG396, SG397, SG398 SG399 SG400 SG401 SG402, SG403, SG404, SG405, SG405 SG407 SG408 SG409 SG410, SG410, SG412, SG412, SG414 SG415 SG416 SG418 SG418, SG419, SG420, SG421, SG422 SG423 SG424 SG425 SG426, SG427, SG428, SG429, SG430 SG431 SG432 SG433 SG434, SG435, SG436, SG437, SG438 SG439 SG439 SG441 SG441, SG443, SG444, SG444, SG446 SG447 SG448 SG449 SG449 SG451, SG452, SG453, SG454 SG455 SG456 SG457 SG458, SG459, SG460, SG461, SG462 SG465 SG468 SG469 SG470, SG471, SG472, SG473, SG474 SG475 SG476 SG477 SG478, SG479, SG480, SG481, SG482 SG483 SG484 SG485 SG486, SG487, SG488, SG489, SG490 SG491 SG492 SG493 SG494, SG495, SG496, SG497, SG498 SG499 SG500 SG501 SG502, SG503, SG504, SG505, SG505, SG506 SG507 SG508 SG509 SG510, SG511, SG512, SG513, SG514 SG515 SG516 SG517 SG518, SG519, SG520, SG521, SG522 SG523 SG524 SG525 SG526, SG527, SG428, SG529, SG530 SG531 SG532 SG533 SG534, SG535, SG536, SG537, SG538 SG539 SG540 SG541 SG542, SG543, SG544, SG545, SG546 SG547 SG548 SG549 SG550, SG551, SG552, SG553, SG554, SG555, SG556, SG557, SG558, SG559, SG560, SG561, SG562, SG563, or combinations thereof.
27. 27. Method to diagnose in vitro a neoplasm originated from hematopoietic cells and / or to predict the evolution of the same according to claim 26, which additionally comprises an optional preliminary step of identifying significant genes for the classification of a shows as associated or not to a specific type of neoplasia originated from hematopoietic cells, previous stage that includes the sub-steps of: a) deciding the possible categories in which the sample can be classified; b) Obtain biological samples from individuals that have previously been assigned by a method different from the one claimed to any of the possible classification categories, so that samples of each of the possible categories are available; c) obtain the total mRNA from each of the samples; d) obtain the corresponding total cRNA, marked by a method that allows its subsequent detection, of at least one aliquot of each of the mRNA samples, aliquot to which is added before obtaining the cRNA at least one sequence of polyadenylated nucleotides of low homology with human genes to act as an internal positive control of the process; e) adding to each of the aliquots of cR A to be used in step f) at least one oligonucleotide of low homology with human genes other than and not complementary to any possible nucleotide sequence that has been added in the step d), to act as a positive hybridization control; f) hybridizing, under stringent conditions, at least one aliquot of total cRNA of each of the samples with at least one microarray comprising at least two copies of each of the oligonucleotides of the group consisting of: SG1, SG2, SG3, SG4 , SG5, SG6, SG7, SG8, SG9, SG10, SG11, SG12, SG13, SG14, SG15, SG16, SG17, SG18, SG19, SG20, SG21, SG22, SG23, SG24, SG25, SG26, SG27, SG
28. 28, SG
29. 29, SG30, SG31, SG32, SG33, SG34, SG35, SG36, SG37, SG38, SG39, SG40, SG41, SG42, SG43, SG44, SG45, SG46, SG47, SG48, SG49, SG50, SG51, SG52, SG53, SG54, SG55, SG56, SG57, SG58, SG59, SG60, SG61, SG62, SG63, SG64, SG65, SG66, SG67, SG68, SG69, SG70, SG71, SG72, SG73, SG74, SG75, SG76, SG77, SG78, SG79, SG80, SG81, SG82, SG83, SG84, SG85, SG86, SG87, SG88, SG89, SG90, SG91, SG92, SG93, SG94, SG95, SG96, SG97, SG98, SG99, SG100, SG101, SG102, SG103, SG104, SG105, SG106, SG107, SG108, SG109, SG110, SG111, SG112, SG113, SG114, SG115, SG116, SG117, SG118, SG119, SG120, SG121, SG122, SG123, SG124, SG125, SG126, SG127, SG128, SG129, SG130, SG131, SG132, SG133, SG134, SG135, SG136, SG137, SG138, SG139, SG140, SG141, SG142, SG143, SG144, SG145, SG146, SG147, SG148, SG149, SG150, SG151, SG152, SG153, SG154, SG155, SG156, SG157, SG158, SG159, SG160, SG161, SG162, SG163, SG164, SG165, SG166, SG167, SG168, SG169, SG170, SG171, SG172, SG173, SG174, SG175, SG176, SG177, SG178, SG179, SG180, SG181, SG182, SG183, SG184, SG185, SG186, SG187, SG188, SG189, SG190, SG191, SG192, SG193, SG194, SG195, SG196, SG197, SG198, SG199, SG200, SG201, SG202, SG203, SG204, SG205, SG206, SG207, SG208, SG209, SG210, SG211, SG212, SG213, SG214, SG215, SG216, SG217, SG218, SG219, SG220, SG221, SG222, SG223, SG224, SG225 SG226, SG227, SG228, SG229, SG230, SG231, SG232, SG233, SG234, SG235, SG236, SG237, SG238, SG239, SG240, SG241, SG242, SG243, SG244, SG245, SG246, SG247, SG248, SG249, SG250, SG251, SG252, SG253, SG254, SG255, SG256, SG257 SG258, SG259, SG260, SG261, SG262, SG263, SG264, SG265, SG266, SG267, SG268, SG269, SG270, SG271, SG272, SG273 SG274, SG275, SG276, SG277, SG278, SG27 9, SG280, SG281 SG282, SG283, SG284, SG285, SG286, SG28 7, SG288, SG289 SG290, SG291, SG292, SG293, SG294, SG29 5, SG296, SG297 SG298, SG299, SG300, SG301, SG302, SG30 3, SG304, SG305 SG306, SG307, SG308, SG309, SG310, SG31 1, SG312, SG313 SG314, SG315, SG316, SG317, SG318, SG31 9, SG320, SG321 SG322, SG323, SG324, SG325, SG326, SG32 7, SG328, SG329 SG330, SG331, SG332, SG333, SG334, SG33 5, SG336, SG337 SG338, SG339, SG340, SG341, SG342, SG34 3, SG344, SG345 SG346, SG347, SG348, SG349, SG350, SG35 1, SG352, SG353 SG354, SG355, SG356, SG357, SG358, SG35 9, SG360, SG361 SG362, SG363, SG364, SG365, SG366, SG36 7, SG368, SG369 SG370, SG371, SG372, SG373, SG374, SG37 5, SG376, SG377 SG378, SG379, SG380, SG381, SG382, SG38 3, SG384, SG385 SG386, SG387, SG388, SG389, SG390, SG39 1, SG392, SG393 SG394, SG395, SG396, SG397, SG398, SG39 9, SG400, SG401 SG402, SG403, SG404, SG405, SG406, SG40 7, SG408, SG409 SG410, SG411, SG412, SG413, SG414, SG41 5, SG416, SG417 SG418, SG419, SG420, SG421, SG422, SG42 3, SG424, SG425 SG426, SG427, SG428, SG429, SG430, SG43 1, SG432, SG433 SG434, SG435, SG436, SG437, SG438, SG43 9, SG440, SG4 1 SG442, SG443, SG444, SG445, SG446, SG44 7, SG448, SG449 SG450, SG451, SG452, SG453, SG454, SG455, SG456, SG457, SG458, SG459, SG460, SG461, SG462, SG465, SG468, SG469, SG470, SG471, SG472, SG473, SG474, SG475, SG476, SG477, SG478, SG479, SG480, SG481, SG482, SG483, SG484, SG485, SG486, SG487, SG488, SG489, SG491, SG492, SG493, SG494, SG495, SG496, SG497, SG498, SG499, SG500, SG501, SG502, SG503, SG504, SG505, SG506, SG507, SG508, SG509, SG510, SG511, SG512, SG513, SG514, SG515, SG516, SG517, SG518, SG519, SG520, SG521, SG522, SG523, SG524, SG525, SG526, SG527, SG428, SG529, SG530, SG531, SG532, SG533, SG534, SG535, SG536, SG537, SG538, SG539, SG540, SG541, SG542, SG543, SG544, SG545, SG546, SG547, SG548, SG549, SG550, SG551, SG552, SG553, SG554, SG555, SG556, SG557, SG558, SG559, SG560, SG561, SG562, SG563, microarray further comprising: a. at least two points corresponding to aliquots of the solvent in which the oligonucleotides were at the moment of their deposition on the surface of the microarray, so that they serve as targets, b. at least two copies of at least one oligonucleotide for each of the polyadenylated sequences added in step d), oligonucleotide whose sequence will correspond to a fragment, other than the polyadenylation zone, of the polyadenylated nucleotide sequence whose evolution in the process has to control; for each of the oligonucleotides added in step e), at least two copies of an oligonucleotide complementary thereto; at least two copies of each member of at least one pair of oligonucleotides in which the sequence of one of the members corresponds to a sequence of the 5 'region and the sequence of the other corresponds to a sequence of the 3' region of the mRNA of a gene that is expressed constitutively in any cell of hematopoietic origin; at least two copies of at least one oligonucleotide of low homology with human genes distinct from any of the oligonucleotides defined in section b. and distinct from any of the synthetic oligonucleotides optionally added in step e); detect and quantify the cR A signal hybridized with each of the copies of each of the oligonucleotides present in the microarray, as well as the signal corresponding to the solvent points; calculate the average level of hybridization intensity of each of the oligonucleotides of the microarray by calculating the average of the intensities of the copies of each of the oligonucleotides; give the hybridization as valid if the following conditions are met: a. the ratio between the mean intensity and the average background of all the oligonucleotides of the microarray is greater than 10; b. the value of the mean variation coefficient of all oligonucleotide replicates should be less than 0.3; c. the mean value of negative control must be less than 2.5 times the average value of the points corresponding to the solvent; d. there is a signal both in the hybridization controls and in the internal positive controls used as process control; normalize the data; eliminate the oligonucleotides with values of mean intensity less background noise less than about 2 times the average value obtained with the points corresponding to the solvent, as well as the oligonucleotides with an interquartile range of normalized intensity throughout the samples less than 0, 3; perform the statistical analysis to find the statistically significant oligonucleotides to differentiate between the different categories and be able to perform the classification of a sample that has not previously been assigned to any category, choosing said oligonucleotides from those that have not been eliminated in the previous steps, until obtain the "n" oligonucleotides which either have a p value lower than a limit chosen from the open range of 0 to 0.05, preferably using a method with the capacity to reduce false positives, or those which better define the established categories; verify that the grouping of the samples according to the differences in the intensities between the different samples detected for the statistically significant oligonucleotides results in the samples being classified in the same categories to which they had previously been assigned by a different method. The method according to claim 27, wherein the microarray comprises at least four copies of each of the oligonucleotides present therein and the average of the intensities of the copies of each of the oligonucleotides that is calculated in h) is a bounded average. The method of claim 28, wherein the normalization is performed using the "variance stabilization normalization" method available in the "vsn" package of R.
30. 30. Method according to any of claims 27 to 29, in which the statistical analysis to find the statistically significant oligonucleotides to differentiate between the different categories is performed using the mt.maxT function of the multtest package of R.
31. 31. Method according to any of claims 27 to 30, wherein a diagnostic device according to claim 24 is used.
32. 32. Method according to claims 27 to 31, comprising an optional step of obtaining a classification function for each sample by arbitrarily assigning the value 0 to one of the possible categories "a" and from value 1 to the other possible category "b "in which the sample can be classified and obtained by logistic regression of a coefficient for each of the oligonucleotides that allow us to calculate a value xi for each sample by means of a function of the type: n Xi = constant +? (coef_oligm * Imni_oligm) m = l where coef_oligm represents the calculated coefficient for a particular oligonucleotide "m" Imni_oligm represents the mean value of normalized intensity obtained in the hybridization of the sample i calculated for the oligonucleotide "m" "m" varies from 1 to "n" n is the number total of oligonucleotides considered significant value "xi" from which the probability "pi" is calculated that a sample "i" belongs to one or another category using the formula pi = 1 / (l + e "xl) and classifying the shows as belonging to the category "a" or wb "according to its corresponding value pi is closer to 0 or 1, respectively.
33. 33. Method according to any of claims 27 to 29, wherein the statistical analysis to find the significant oligonucleotides to differentiate between the different categories is done using the "Nearest Shrunken Centroids" method.
34. 34. The method according to any of claims 27 to 33, wherein the biological samples analyzed in vitro are peripheral blood samples.
35. 35. The method according to claim 34, wherein a leukemia is diagnosed in vitro or the evolution thereof is predicted.
36. 36. The method of claim 35, wherein an individual is diagnosed in vitro with chronic lymphatic leukemia.
37. 37. The method according to claim 35, wherein the evolution of chronic lymphatic leukemia in a subject is predicted in vitro by classifying a blood sample drawn from it as "associated with stable chronic lymphatic leukemia" or as "associated with chronic lymphocytic leukemia". progressive ".
38. 38. Method to diagnose in vitro a neoplasm originated from hematopoietic cells and / or to predict in vitro the evolution of the same that includes the detection in vitro and the statistical analysis of the level of expression of at least one significant gene to classify the sample as belonging to a healthy individual or associating it with some type of neoplasia originated from hematopoietic cells according to claim 26, in which the neoplasm that is diagnosed and / or whose evolution is predicted is a leukemia.
39. 39. Method according to claim 38, in which the evolution of chronic lymphatic leukemia is diagnosed and / or predicted.
40. 40. Method for diagnosing in vitro chronic lymphocytic leukemia and / or predicting in vitro its evolution according to claim 39, wherein the in vitro detection of the level of expression of at least one significant gene is performed from peripheral blood samples.
41. 41. Method for diagnosing in vitro chronic lymphocytic leukemia according to claim 40, wherein the subjects from which the corresponding blood samples have been extracted are classified either in the category of subject not suffering from CLL or in the category of subject that have an LLC
42. 42. Method for diagnosing in vitro chronic lymphatic leukemia according to claim 41, wherein the The classification of the subjects is carried out after the in vitro detection and the statistical analysis of the level of expression in the corresponding blood samples of at least the genes CD79A, FAIM3, HLA-DRA, HLA-DRB3, HLA-DQA1.
43. 43. Method for diagnosing in vitro chronic lymphatic leukemia according to claim 42, wherein the classification of the subjects is carried out after in vitro detection and statistical analysis of the level of expression in the corresponding blood samples additionally of the IRF8 genes and C0L3A1. 2
44. 44. Method for diagnosing in vitro chronic lymphocytic leukemia according to claim 43, wherein the in vitro detection and statistical analysis of the expression level of the genes CD79A, FAIM3, HLA-DRA, HLA-DRB3, HLA-DQA1, IRF8 and C0L3A1 is performed by evaluating the corresponding Q mRNA by hybridization of its corresponding cRNA using oligonucleotides SG117, SG428, SG459, SG507, SG508, SG461 and SG493 as probes.
45. 45. Method for diagnosing in vitro chronic lymphocytic leukemia according to claim 44, wherein the oligonucleotides are part of a composition in the form of a microarray.
46. 46. Method for diagnosing in vitro chronic lymphocytic leukemia according to claim 45, wherein the evaluation of the hybridized cRNA is carried out thanks to the previous labeling of cRNA with biotin, the staining of the microarray hybridized with streptavidin conjugated with a fluorophore and the detection of the signal emitted by said fluorophore.
47. 47. Method for diagnosing in vitro chronic lymphocytic leukemia according to claim 46, wherein fluorophore is Cy3.
48. 48. Method for diagnosing in vitro chronic lymphocytic leukemia according to claim 47, wherein the classification of a subject from whom the sample has been extracted i analyzed in the category of subject not suffering from CLL or in the category of subject suffering from CLL is done by calculating for that subject a probability value pi = l / (l + e "xl) after obtaining its corresponding value of x¿ by the formula Xi = -719,241486 + (2.44756372 * Imni_CD79A) + (7, 38657611 * Imni_FAIM3) + (23,1465464 * Imni_HLA-DRA) + (43,6287742 * Imni_IRF8) - (19,3978182 * Imrii_C0L3Al) - (2,80282646 * Imni_HLA-DRB3) + (49,5345672 * Imni_HLA-DQAl) a formula in which each of the values denominated by the abbreviation "Imni" followed by the abbreviation of a gene refers to the mean normalized intensity value obtained after detecting the hybridization signal corresponding to the oligonucleotide that is being used as a probe to evaluate the expression of the said gene and classifying the subject as a subject who does not have an LLC if the value of pi is less than 0.5 and as a subject who has an LLC if the value of pi is greater than 0.5.
49. 49. Method for predicting in vi tro the evolution of the disease in a subject suffering from chronic lymphatic leukemia according to claim 40, in which the subjects from whom the corresponding blood samples have been extracted are classified or in the category of subject with stable LLC or in the subject category with progressive LLC.
50. 50. Method for predicting in vitro the evolution of the disease in a subject suffering from chronic lymphatic leukemia according to claim 49, in which the classification of the subjects is carried out after the in vitro detection and the statistical analysis of the level of expression in the corresponding blood samples of at least the genes PSMB4, FCER2 and POU2F2.
51. 51. Method for predicting in vitro the course of the disease in a subject suffering from chronic lymphocytic leukemia according to claim 50, in which the classification of the subjects is carried out after the in vitro detection and the statistical analysis of the level of expression in the corresponding blood samples additionally from at least one gene selected from the group consisting of ODC1, CD79A, CD2, CD3E, CD5, MS4A1, EIF4E, FHIT, NR3C1, LCP1, MAPK10, ABCC5, XRCC3, CML66, PLZF, RBP4.
52. 52. Method for predicting in vitro the course of the disease in a subject suffering from chronic lymphocytic leukemia according to claim 51, in which the classification of the subjects is carried out after in vitro detection and the statistical analysis of the level of expression in the corresponding blood samples from at least the genes of the group consisting of PSMB4, FCER2, P0U2F2, ODC1, CD79A, CD2, CD3E, CD5, MS4A1, EIF4E, FHIT, NR3C1, LCP1, MAPK10, ABCC5, XRCC3, CML66, PLZF, RBP4 .
53. 53. Method for predicting in vitro the evolution of the disease in a subject suffering from chronic lymphatic leukemia according to any of claims 51 or 52, in which in vitro detection and statistical analysis of the expression level of the genes examined is carried out by evaluating the corresponding mRNA by hybridization of its corresponding cRNA using as probes the corresponding oligonucleotides selected from group consisting of SG26, SG216, SG366, SG31, SG177, SG194, SG195, SG197, SG213, SG293, SG301, SG309, SG33, SG343 , SG357, SG439, SG452, SG555, SG556.
54. 54. Method for predicting in vitro the course of the disease in a subject suffering from chronic lymphocytic leukemia according to claim 53, wherein the oligonucleotides are part of a composition in the form of a microarray.
55. 55. Method for predicting in vitro the evolution of the disease in a subject suffering from chronic lymphocytic leukemia according to claim 54, in which the evaluation of the corresponding mRNA of the sample analyzed by detecting the corresponding cRNA hybridized to the corresponding oligonucleotide is done thanks to the previous labeling of the cRNA with biotin, the staining of the microarray hybridized with streptavidin conjugated with a fluorophore and the detection of the signal emitted by said fluorophore.
56. 56. Method for predicting in vitro the evolution of the disease in an individual suffering from chronic lymphatic leukemia according to claim 55, wherein the fluorophore is Cy3.
57. 57. Use of an expression level evaluation device of at least one gene of the group consisting of PSMB4, FCER2, POU2F2, ODC1, CD79A, CD2, CD3E, CD5, MS4A1, EIF4E, FHIT, NR3C1, LCP1, MAPK10, ABCC5 , XRCC3, CML66, PLZF, RBP4, CD79A, FAIM3, HLA-DRA, HLA-DRB3, HLA-DQA1, IRF8 and COL3A1 for the in vitro diagnosis of the existence of chronic lymphatic leukemia in a subject and / or for prognosis in Vitro of the evolution of chronic lymphatic leukemia in a subject.
58. 58. Use of a device for assessing the level of gene expression according to claim 57, wherein the level of expression of at least one gene of the group consisting of CD79A, FAIM3, HLA-DRA, HLA-DRB3, HLA is evaluated -DQA1, IRF8 and C0L3A1 for the in vitro diagnosis of the existence of chronic lymphatic leukemia in a subject.
59. 59. Use of a device for assessing the level of gene expression according to any of claims 57 and 58, in which the level of expression of at least the genes CD79A, FAIM3, HLA-DRA, HLA-DRB3, HLA is evaluated -DQA1 for the in vitro diagnosis of the existence of chronic lymphatic leukemia in a subject.
60. 60. Use of a device for assessing the level of gene expression according to claim 59, wherein the level of expression of the minus IRF8 and COL3A1 genes for the in vitro diagnosis of the existence of chronic lymphatic leukemia in a subject is further evaluated .
61. 61. Use of a device for assessing the level of gene expression according to claim 57, wherein the level of expression of at least one gene of the group composed of PSMB4, FCER2, POU2F2, 0DC1, CD79A, CD2E, CD3E is evaluated , CD5, MS4A1, EIF4E, FHIT, NR3C1, LCP1, MAPK10, ABCC5, XRCC3, CML66, PLZF, RBP4, to predict in vitro the evolution of the disease in a subject suffering from chronic lymphatic leukemia.