WO2018211160A1 - Method for classifying medulloblastoma patients into molecular sub-groups - Google Patents

Method for classifying medulloblastoma patients into molecular sub-groups Download PDF

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WO2018211160A1
WO2018211160A1 PCT/ES2018/070347 ES2018070347W WO2018211160A1 WO 2018211160 A1 WO2018211160 A1 WO 2018211160A1 ES 2018070347 W ES2018070347 W ES 2018070347W WO 2018211160 A1 WO2018211160 A1 WO 2018211160A1
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shh
wnt
methylation
group
panel
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Cinzia Emilia LAVARINO
Soledad GOMEZ GONZALEZ
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Hospital Sant Joan De Deu
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer

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  • the present invention has its field of application within the health sector, mainly in the Pediatric Oncology "and” Molecular Biology "sectors.
  • the present invention relates to a method of classifying tumors into molecular subgroups of clinical interest. More specifically, the present invention contemplates an in vitro method for the classification of a patient with medulloblastoma in one of the molecular subgroups WNT, SHH, Group 3 and Group 4. Background of the invention
  • Medulloblastoma is a highly malignant embryonic tumor of neuroepithelial origin that was first described as a tumor of the central nervous system in 1925. It is the most common malignant brain tumor in the pediatric age and represents approximately 20% of pediatric tumors of the central nervous system (Gilbertson RJ and Ellison DW. The Origins of Medulloblastoma
  • MBs of pediatric age originate in the cerebellar vermis, and protrude into the fourth ventricle, with the remaining 25% located in the cerebellar hemispheres. About 30% of pediatric cases have metastases at the time of diagnosis. Most of Metastases develop in the central nervous system (cranial or medullary), while the spread to extracranial organs is very rare at diagnosis. In a minority of patients, MB is associated with Gorlin syndrome (www.orpha.net, ORPHA377), adenomatous polyposis or with Li-Fraumeni syndrome (www.orpha.net; ORPHA616).
  • the differential diagnosis of MB is posed, macroscopically, with other tumors of the posterior fossa (cerebellar vermis) such as pilocytic astrocytoma and ependymoma.
  • Other tumors of the posterior fossa such as pilocytic astrocytoma and ependymoma.
  • Atypical rhabdoid teratoid tumor must also be considered in certain situations. From a microscopic point of view, confusion with pyrocytic astrocytoma is rare, with the exception of dedifferentiated tumors, such as malignant astrocytomas or glioblastomas of this region.
  • the difficulty may arise in cases of high cell density, in which small gliovascular systems can induce confusion with rosettes or pseudo-soils and with other secondary structures characteristic of ependymomas (Gilbertson RJ and Ellison DW. Origins of
  • MB Medulloblastoma Subtypes. Annual Review of Pathology Mechanisms of Disease 2008, 3: 341-365; Escalona-Zapata J. Tumors of the Central Nervous System. Editorial Complutense 1996).
  • the treatment of MB depends on the age, the spread of the disease, the histological variant and the molecular characteristics of the tumor.
  • treatment for MB includes surgical resection, craniospinal radiotherapy (in patients older than 3 years) and adjuvant chemotherapy.
  • the MB represents a heterogeneous group of cerebellar tumors characterized by having diverse clinical behavior, histopathology, biology and cure rates (Gajjar A et al. Pediatric Brain Tumors: innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape. Journal of Clinical Oncology 2015 ; 33 (27): 2986-2998).
  • the clinical stratification schemes used for decades have been based solely on clinical characteristics (size and state of dissemination of the tumor), on the age at diagnosis, the degree of surgical resection and the histology of the tumor.
  • the age of the patient at the time of diagnosis is a determining factor as it is reflected in the aggressive behavior of tumors in patients younger than 3 years (DeSouza RMet al.
  • MBs are considered highly malignant, correspond to a grade IV classification of tumors of the central nervous system of the World Health Organization (WHO). According to the 2007 WHO classification, histologically, MBs are classified into three large groups that include the classic subtype, desmoplastic / nodular medulloblastoma (MBEN) and the giant / anaplastic cell subtype (ACL).
  • WHO World Health Organization
  • the classic subtype is the most frequent (66%), characterized by being composed of undifferentiated small cells, densely packed, which are stained blue with hematoxylin (basophilic) with hyperchromatic nuclei and sparse cytoplasm that frequently form homer rosettes.
  • Classic MB grows from the most central part of the cerebellum (cerebellar vermis) (Louis DN et al. WHO Classification of Tumors of the
  • the anaplastic subtype (15%) is characterized by a marked nuclear pleomorphism and cellular molding, and the large cell variant (2-4%) has a monomorphic cell population with a prominent nucleolus. Both variants are characterized by high cell proliferation, abundant apoptosis and a more unfavorable prognosis (Louis DN et al. WHO Classification of Tumors of the Central Nervous System. Lyon: IARC 2007).
  • the clinical-pathological behavior of MB is a reflection of the underlying genetic-biological characteristics of the tumor.
  • genomic studies massive genome sequencing, transcriptome and DNA methyloma analysis and study of chromosomal alterations
  • WNT wingless
  • SHH Sonic hedgehog
  • Group 3 Group 4
  • WNT wingless
  • SHH Sonic hedgehog
  • the best known subgroup of MB is the WNT subgroup due to its excellent prognosis compared to the other subgroups.
  • the overall survival rates of WNT MBs can exceed 90%, where those patients who die are mostly due to complications associated with treatment or secondary malignancies (Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123: 465-472).
  • This subgroup represents approximately 10% of patients with MB. It usually affects older patients (mean age 10 years), most have a classical histology and rarely present with metastases. 80-85% are associated with the presence of chromosome 6 monosomy (Gajjar A et al.
  • the SHH subgroup represents approximately 25% of MBs, mostly of nodular desmoplastic histology.
  • the prognosis is quite variable and dependent on the patient's age: young children with SHH treated exclusively with chemotherapy have an excellent prognosis, while older patients with SHH associated with mutations in TP53 have an unfavorable prognosis, especially if they have amplification of the MYCN and GLI2 genes.
  • Group 3 and Group 4 constitute 25% and 35% of the MBs, respectively. Even though they are genetically distinct, these two subgroups have numerous common genetic alterations. To date, no specific pattern has been found that distinguishes them in Group 3 and Group 4 conclusively. Both groups are more frequent in children, and isochromosome 17q only occurs in these tumors, being more frequent in Group 4 (80% vs. 26%).
  • the MBs of Group 3 and Group 4 show a low frequency of recurrent mutations.
  • the activation of the expression of the proto-oncogenes family GFI1 and GFI1B by means of a "hijacking" mechanism of "enhancers" (enhancers) has been described.
  • This GFI1 / GFI1B genetic alteration is active in approximately 40% and 10% of Group 3 and Group 4 tumors, respectively.
  • Group 3 MBs are associated with ACL histology and metastatic dissemination (50%). They are also characterized by MYC overexpression (17% show MYC amplification). The presence of metastatic disease, isochromosome 17q and amplification of MYC gives Group 3 an unfavorable prognosis.
  • Group 4 tumors usually have a classical histology, and sometimes ACL histology. Patients have an intermediate prognosis. The amplification of the MYCN oncogene in these tumors, unlike SHH is not associated with an unfavorable prognosis.
  • RNA methylation profile Two strategies are currently used for the classification of these tumors: 1) technology based on the quantification of RNA levels through the use of the NanoString nCounter System (NanoString Technologies, Inc.) and 2) high density microarray technology (/ Illuminates Infinium Human Methylation 450K BeadChip array (HM450K)) to analyze the complete genome (DNA) methylation profile of tumors.
  • NanoString nCounter System NanoString Technologies, Inc.
  • HM450K high density microarray technology
  • NanoString nCounter System technology is based on a non-enzymatic analysis with sequence-specific probes for the digital quantification of the levels of multiple target RNAs in a sample.
  • the Taylor MD group at the Sick Children's Hospital in Toronto identified 22 genes whose levels of expression allow classifying MBs (Northcott PA et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathologica 2012, 123: 615-626).
  • the same group has developed an assay (a library of probes for RNA quantification) that allows quantification of Expression levels of the 22 genes using a digital analyzer.
  • the HM450k microarray interrogates the methylation status of more than 450,000 cytosines throughout the entire genome. It covers 96% percent of the cytosine-guanine dinucleotide islands (CpG) of the entire genome, multiple "shores” CpG (shores) and isolated CpGs located in both intragenic and intergenic regions. This methodology has proven to be reliable for the classification of MBs in subgroups with both fresh frozen tissue and formalin fixed and included in paraffin.
  • HM450k usefulness in clinical practice is limited by the extremely high number of data generated by the microarray (more than 450,000 data), the difficulty of data processing (ie quality control of fragment hybridization of DNA to the array, control of the robustness of the signal of said sequences, correction of the signal due to non-specific hybridization that affects the sensitivity and specificity of the result, normalization of the specific signal to contain technical errors) and the difficulty of mass analysis of large amounts of methylation data that necessitate the use of computational technology, bioinformatics methods
  • microarray technology has a high economic cost. Therefore, this classification strategy for MBs using HM450K microarray technology is centralized at the German Cancer Center in Heidelberg, Germany.
  • medulloblastoma an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas. Acta Neuropathologica 2012, 123: 473-484; Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123: 465-472; Northcott PA et al Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 2012, 488: 49-56; Northcott PA. et al. Medulloblastomics: the end of the beginning. Nature Reviews Cancer 2012, 12: 818-834; Northcott PA et al.
  • G3-G4 in combination, can be used as a marker for stratification in the four main molecular subgroups of patients with MB.
  • DNA methylation is a post-replicative modification that involves the covalent attachment of a methyl group [-CH3] to the carbon 5 position of cytosines that precede guanines (cytosine-guanine dinucleotides or CpG). These are not uniformly distributed in the human genome, there are regions where their concentration is high called "cytosine-guanine islands" (CpG).
  • CpG cytosine-guanine islands
  • DNA methylation is a very well characterized process. When cells divide, in addition to inheriting the sequence of their genome, they inherit the methylation patterns present in the cell of origin. Unlike genetic information, which is transmitted from stem cells to daughters with a low variation rate, epigenetic information presents greater dynamics.
  • Genome methylation in CpG dinucleotides is an epigenetic mechanism of gene regulation involved in primary cellular processes for embryonic development of mammals (Smith ZD et al. DNA methylation: roles in mammalian development. Nat Rev Genet (2013) 14 (3) , 204-20).
  • Epigenetics comprises all those cellular mechanisms (DNA methylation, histone modifications or non-coding RNA) that influence genetic regulation, without altering the sequence of the genes or genome.
  • Epigenetic processes constitute an essential programming for development and differentiation. During development, the genome undergoes modifications that are crucial for the determination of cell lineage and differentiation (Smith ZD et al. DNA methylation: roles in mammalian development. Nat Rev Genet (2013) 14 (3), 204-20).
  • BS Sodium bisulfite
  • locus-specific analysis techniques are, among others, the specific methylation by PCR (MSP), bisulfite sequencing (BSP) and bisulfite pyrosequencing.
  • MSP specific methylation by PCR
  • BSP bisulfite sequencing
  • the authors of the invention have developed a strategy for the molecular classification of MB based on the analysis of the methylation patterns of a group of differentially methylated cytosines (Panel WNT-SHH and Panel G3-G4).
  • the methylation pattern of such cytosines can be analyzed by various applicable techniques for DNA methylation analysis.
  • PCR DNA polymerase chain reaction
  • the authors of the invention have demonstrated that the proposed strategy for the molecular classification of MB can be applied to DNA extracted from all types of samples with an adequate representation of tumor DNA, such as fresh tissue biopsy. (F) and frozen and preserved at -80 ° C (FF, from English fres / 7 frozer ⁇ ) or fixed in 10% buffered formalin and embedded in paraffin (FFPE, from English formalin fixed, paraffin-embedded).
  • F fresh tissue biopsy.
  • FF frozen and preserved at -80 ° C
  • FFPE 10% buffered formalin and embedded in paraffin
  • this classification strategy is given both by the reduced number of cytosines that make up Panel WNT-SHH and Panel G3-G4, as well as viability at the technical level (pyrosequencing, MSP, BSP or all those techniques that allow determining directly or indirectly, the pattern of methylation of a sequence of interest), the applicability to small tumor tissue biopsies obtained in F / FF, FFPE and / or liquid biopsies, high precision, speed, easy interpretation, reproducibility of the results , and for the low economic cost.
  • Figure 1 Standardization, quality control and filtering of raw methylation data generated by high density microarray (microarray) technology Illuminates Human Methylation BeadChip 450K. Study cohort, 106 medulloblastomas in F / FF.
  • A Density diagram of the methylation data set;
  • B Graphical representation of the quality control of bisulfite conversion of DNA;
  • C Diagram of densities of normalized data using the SWAN methodology.
  • Figure 2. Unsupervised analysis of the methylation levels of the study cohort, 106 medulloblastomas in F / FF. The set of methylation profiles of all samples are defined.
  • A Analysis of the distribution of the variability (density plof) of DNA methylation of the samples;
  • B Principal Component Analysis (ACP) and
  • C hierarchical clustering analysis of all CpGs with standard deviation greater than or equal to 0.3 (5,904 CpGs).
  • Figure 3 Unsupervised analysis using the set of the nine cytosines that make up the WNT-SHH Panel. Study cohort, 106 samples of medulloblastomas in F / FF.
  • B Graphical representation (violin plot) of the differential methylation pattern of the WNT-SHH Panel cytosines in the WNT, SHH and non-WNT / non-SHH subgroups;
  • C Comparison of the cytosine methylation values of the WNT-SHH Panel in other normal and tumor tissues.
  • GS adrenal gland
  • ESC embryonic stem cells
  • IPSC induced pluripotent stem cells
  • NPSC progenitor neuronal cells
  • GPSC progenitor glial cells
  • GB glioblastoma
  • DIPG diffuse intrinsic pontine glioma
  • PA pyrocytic astrocytoma (pilocytic astrocytoma);
  • ATRT atypical teradoid / rhabdoid tumor (atypical teradoid / rhabdoid tumor);
  • NB neuroblastoma;
  • GN ganglioneuroma.
  • GS adrenal gland
  • ESC embryonic stem cells
  • IPSC induced pluripotent stem cells
  • NPSC progenitor neuronal cells
  • GPSC progenitor glial cells
  • GB glioblastoma
  • DIPG diffuse protrusion glioma
  • PA pyrocytic astrocytoma (pilocytic astrocytoma); ATRT: atypical teradoid / rhabdoid tumor (atypical teradoid / rhabdoid tumor); NB: neuroblastoma; GN: ganglioneuroma.
  • Figure 5. Validation of Panel WNT-SHH and Panel G3-G4 by using the HM450k DNA methylation database.
  • Validation cohort 169 samples of FFPE medulloblastoma.
  • A Analysis not supervised by ACP using the set of the nine cytosines that make up the WNT-SHH Panel (9 CpGs);
  • B ACP of the cytosine methylation values identified in Panel G3-G4 (8 CpGs) in FFPE medulloblastoma samples.
  • FIG. 6 Analysis of the methylation pattern of the WNT-SHH Panel by bisulfite sequencing methodology (BSP) in F / FF tissue DNA and medulloblastoma FFPE.
  • BSP bisulfite sequencing methodology
  • the circles show the differential methylation pattern of the nine cytosines of interest of the WNT-SHH Panel, (red) identifying status and (green) excluding status of the subgroup.
  • B SHH Subgroup and
  • C Non-WNT / Non-SHH Subgroup.
  • FIG. 7 Graphic example of the levels of methylation of the cytosines of the WNT-SHH Panel obtained by pyrosequencing by bisulfite in DNA of tissue F / FF and FFPE of medulloblastoma. Description of the invention
  • the present invention has as main objective to identify a marker for the classification of patients with medulloblastoma (MB) that constitutes a more easily applicable test than the existing classification systems, which is reproducible and with a good cost-effectiveness in clinical practice.
  • MB medulloblastoma
  • two cytosine panels are defined which, in combination , act as an effective molecular classification marker for patients with MB in four molecular subgroups: WNT, SHH, Group 3 and Group 4.
  • the authors of the present invention have confirmed that there is in MB an association of the DNA methylation pattern with the genetic entities WNT, SHH, and non-WNT / non-SHH. From these methylation patterns, the authors have selected a first panel of nine cytosines, with a differential methylation pattern, which is significantly and accurately associated with each of the WNT, SHH, and non-WNT / no subgroups. -SHH. They have shown that this panel of nine cytosines (hereinafter WNT-SHH Panel) (Table 1) is effective in establishing the three genetic entities defined by WHO (2016): WNT, SHH and Non-WNT / non-SHH Group .
  • cytosines have the ability to correctly classify MBs in these subgroups, such combinations being able to represent potential markers suitable for the classification of these tumors.
  • the WNT-SHH panel consists of 9 cytosines called WNT1_MB, WNT2_MB, WNT3_MB, N-WS1_MB, N-WS2_MB, N-WS3_MB, SHH1_MB, SHH2_MB and SHH3_MB.
  • Each MB molecular subgroup (WNT, SHH and non-WNT / non-SHH) is specifically and uniquely associated with a differential methylation pattern of the cytosines of the WNT-SHH Panel.
  • Each cytosine shows a specific bimodal methylation pattern: very high levels of methylation (average methylation value ⁇ 80%) or conversely, very low levels (average methylation value ⁇ 17%) for each of the subgroups.
  • Those tumors with a methylation pattern with high values in the cytosines WNT1_MB and WNT2_MB and low levels of methylation in WNT3_MB are specifically and univocally associated with the WNT subgroup of MBs.
  • This pattern defines the SHH subgroup univocally and directly.
  • High values in N-WS1_MB and N-WS2_MB, and low in N-WS3_MB are indicators of a tumor that belongs to the non-WNT / non-SHH subgroup of MB.
  • the reference methylation patterns for the WNT-SHH panel can be seen in the schematic table described below (Table 2). Table 2. Reference methylation pattern of the cytosines that constitute the WNT-SHH Panel for the WNT, SHH and non-WNT / non-SHH subgroups.
  • the "+” symbol represents very high levels of methylation (average methylation value ⁇ 80%), while the "-” symbol represents very low levels of methylation (average methylation value ⁇ 17%).
  • Panel G3-G4 a second panel of 8 cytosines as a marker to effectively differentiate the two genetic entities Group 3 and Group 4, currently included provisionally in the WHO non-WNT / non-SHH subgroup.
  • cytosines have the ability to correctly classify MBs in these subgroups, such combinations being able to represent potential markers suitable for the classification of these tumors.
  • Panel G3-G4 consists of 8 cytosines called Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB.
  • Each molecular subgroup of MB is specifically and uniquely associated with a differential methylation pattern of the cytosines of Panel G3-G4.
  • Each cytosine shows a specific bimodal methylation pattern: very high levels of methylation (average methylation value ⁇ 75%) or conversely, very low levels (average methylation value ⁇ 20%) for each of the subgroups.
  • Those tumors with a methylation pattern with high values ( ⁇ 75%) in the cytosines Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB they are specifically and uniquely associated with the subgroup Group 3 of MBs. While low values in cytosines Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB are indicators of a tumor belonging to subgroup Group 4.
  • the reference methylation patterns for panel G3-G4 can be seen in the schematic table described below (Table 4).
  • Cytosine ID Group 3 Cytosine ID Group 4
  • the "+” symbol represents very high levels of methylation (average methylation value ⁇ 75%), while the "-” symbol represents very low levels of methylation (average methylation value ⁇ 20%).
  • the methylation profile of the cytosines of the WNT-SHH panel (and its different combinations) is contemplated for use as a marker for the classification of patients with MB in the three molecular subgroups defined by WHO (2016): WNT, SHH and Non-WNT / non-SHH Group. Additionally, for those MBs classified as non-WNT / non-SHH with the WNT-SHH Panel, the cytosine methylation profile of Panel G3-G4 (and its different combinations) is contemplated for use as a marker for the classification of MB patients in the molecular subgroups Group 3 and Group 4.
  • the analysis of the methylation pattern of the proposed cytosines allows to contrast the levels of differential methylation between MB subgroups with different clinical behavior, which allows classifying the tumors according to their clinical evolution, and establishing the most appropriate treatment for each patient.
  • an in vitro method for the classification of a patient with medulloblastoma in one of the molecular subgroups WNT, SHH and non-WNT / non-SHH group comprising the following steps: a) Analysis of the levels of methylation of the cytosines WNT1_MB, WNT2_MB, WNT3_MB, N-WS1_MB, N-WS2_MB, N-WS3_MB, SHH1_MB, SHH2_MB and SHH3_MB, which form the WNT-SHH panel, or a combination thereof, in the DNA extracted from a biological sample isolated from the patient, and b) Classification of the patient in one of the WNT, SHH and non-WNT / non-SHH molecular subgroups based on the levels of cytosine methylation analyzed in the WNT- panel SHH, according to the reference values in Table 2.
  • step b) of the method of the invention as non-WNT / non-SHH, the following additional steps are carried out: c) Analysis of the levels of methylation of cytosines Gr3- A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB, or a combination thereof, which form panel G3-G4, in the DNA extracted from the biological sample isolated from the patient, and
  • Another method of the invention contemplates the method for classifying a patient with medulloblastoma into one of the molecular subgroups WNT, SHH, Group 3 and Group 4, which comprises:
  • the method of classification of the invention can be performed by various molecular methodologies and applicable to various types of tissue. In this way, the method of the invention allows its application in the clinical practice of most hospital centers that treat pediatric tumors of the nervous system.
  • the biological sample used is tumor tissue.
  • the molecular classification method comprises obtaining a sample of medulloblastoma tumor tissue for the analysis of the cytosine methylation pattern of the Panel.
  • the patient's tumor sample represents a portion of the tumor piece obtained by surgery or a biopsy of the tumor tissue.
  • the tumor sample used for carrying out the method of the invention has a viable tumor cell content greater than 70% (determined by a pathologist).
  • This sample can be obtained either from fresh tumor biopsy without fixing (F), or from frozen tumor biopsy (FF) stored at -80 ° C or fixed in 10% buffered formalin and embedded in Paraffin (FFPE).
  • F fresh tumor biopsy without fixing
  • FF frozen tumor biopsy
  • FFPE 10% buffered formalin and embedded in Paraffin
  • the biological sample is fresh tumor tissue (F).
  • the method of MB classification comprises using a fresh tumor tissue sample of medulloblastoma for the analysis of the methylation pattern of the cytosine combination of Panel WNT-SHH and Panel G3-G4.
  • the biological sample is frozen tumor tissue (FF) and stored at -80 ° C until use.
  • the MB classification method comprises using a sample of frozen stored tumor tissue of MB for the analysis of the methylation pattern of the different combinations of cytosines of Panel WNT-SHH and Panel G3-G4.
  • the biological sample is tumor tissue fixed in 10% buffered formalin and embedded in paraffin (FFPE), whereby samples obtained in a standard pathology laboratory can be evaluated.
  • FFPE buffered formalin and embedded in paraffin
  • the quantification / analysis of the levels of methylation of the cytosines that constitute the invention can be carried out by means of techniques that allow determining directly or indirectly the methylation status of a sequence of interest.
  • the cytosine methylation pattern of Panel WNT-SHH and / or Panel G3-G4 can be analyzed by various techniques applicable to DNA methylation analysis, such as microarray technology and molecular techniques based on conversion DNA with sodium bisulfite, supplemented by amplification by a DNA polymerase chain reaction (PCR) and sequencing methods (bisulfite sequencing and bisulfite pyrosequencing).
  • Conversion of DNA with sodium bisulfite (NaHSC) is the initial step of several techniques, most of which are complemented by amplification by a DNA polymerase chain reaction (PCR).
  • PCR is a technique of selective in vitro amplification of a specific DNA fragment.
  • the method is based on a phase of denaturation of the DNA double helix and binding specific of two oligonucleotides (primers) that flank the region to be amplified and serve as primers to initiate fragment synthesis.
  • the extension of the chain from the primers is obtained by the action of a specific polymerase that supports high temperatures without denaturing. This 3-step process is repeated for 25-40 cycles in a specific device (thermocycler) so that an exponential amplification of the fragment of interest is achieved.
  • Bisulfite induces deamination of unmethylated cytosines which become uracils, while 5-methyl cytosines are unaffected and remain cytosines. Then proceed with the amplification with
  • any method to detect a nucleotide change can be used to identify methylation in the sequence of interest.
  • Bisulfite-specific sequencing allows mapping of allele-specific methylations in cytosines of interest, adding the possibility of observing methylations, in addition to the nucleotide sequence.
  • the bisulfite treated sequence is compared with the control sequence that has not been subjected to the action of bisulfite. Those cytosines that were methylated will appear after PCR and sequencing as cytosines, while in the sample where bisulfite has transformed them into uracil, they will be observed as a thymine (Darst RP et al. Bisulfite Sequencing of DNA. Current Protocols of Molecular Biology (2010) doi: 10. 1002/0471142727. Mb0709s91).
  • Bisulfite sequencing is a variant of automated sequencing, according to the Sanger method.
  • Nucleic acid sequencing according to the Sanger method is a methodology used to determine the order of nucleotides in a DNA fragment.
  • the principle of the Sanger method is the use of dideoxynucleotide triphosphates (ddNTPs) (Sanger F, Nicklen S and Coulson AR. (1977) DNA sequencing with chain-terminating inhibitor. Proc Nati Acad Sci USA, 74 (12): 5463-5467) . These lack the 3 'carbon hydroxyl group and its use in a DNA elongation reaction implies that when it is incorporated into the chain it cannot continue elongation, producing several truncated DNA fragments of variable length.
  • ddNTPs dideoxynucleotide triphosphates
  • the identity of the nucleotide that terminates the chain in each position can be identified by performing four separate reactions using in each of them a different ddNTP (ddATP, ddCTP, ddTTP or ddGTP) (Franga LT et al. A review of DNA sequencing techniques. Q Rev Biophys 2002; 35 (2): 169-200).
  • ddNTP ddATP, ddCTP, ddTTP or ddGTP
  • fluorescently labeled ddNTPs are used, each with a different fluorophore, which allows for a single sequence reaction that includes all ddNTPs (Franga LT et al).
  • the process has been automated. To determine the DNA sequence, the synthesis mixture is loaded into an automated sequencing machine based on capillary electrophoresis.
  • the analysis of the levels of methylation of the cytosines of interest, in DNA previously treated with bisulfite is carried out by specific sequencing of bisulfite treated DNA (BSP).
  • BSP bisulfite treated DNA
  • Pyrosequencing is a method of DNA sequencing that allows quantifying in real time the release of pyrophosphates (PPi) that takes place at the moment when nucleotides are incorporated into the synthesis reaction of the
  • DNA It starts, as in the Sanger method, of a sequence of interest and specific primers, with enzymes and substrate, and unlabeled nucleotides.
  • DNA polymerase binds a dNTP releasing PPi in the process.
  • the enzyme ATP-sulfurylase converts PPi into ATP with the help of adenosine phosphosulfate (APS).
  • Luciferase converts ATP into light, with the help of luciferin.
  • intensity peaks that allow you to read the DNA sequence.
  • the analysis of DNA methylation patterns by pyrosequencing combines the simplicity of the protocol with reproducibility, specificity and analysis accuracy, comparable with high resolution methodologies. Pyrosequencing of bisulfite treated DNA allows a quantitative and precise analysis of methylation based on sequencing by synthesis.
  • the analysis of the levels of methylation of the cytosine combinations that constitute Panel WNT-SHH and / or Panel G3-G4 is carried out by the pyrosequencing methodology of the DNA converted by bisulfite.
  • this molecular classification strategy is given both by the reduced number of cytosines that make up Panel WNT-SHH and Panel G3-G4, as well as viability at the technical level (pyrosequencing, BSP or other techniques that allow direct determination or indirectly the state of DNA methylation), the applicability to small tumor tissue biopsies obtained F and / or FF (F / FF) and / or FFPE, the high precision, speed, easy interpretation and reproducibility of the results, and by The low economic cost.
  • kits for carrying out the in vitro method for the classification of a patient with medulloblastoma in one of the molecular subgroups WNT, SHH and non-WNT / non-SHH group comprising:
  • the oligonucleotide set used to analyze the methylation status of the cytosines that constitute the WNT-SHH Panel, using BSP methodology, are specific sequencing primers for the cytosines of interest (problem cytosines and cytosines for the control of reaction efficiency of DNA conversion with sodium bisulfite).
  • the oligonucleotides employed are selected from those that have the sequences shown in SEQ ID No 1-18, specific for the cytosine problem, and SEQ ID No 48, 49, 51, 52, 54, 55, 57, 58, 60, 61, 63, 64, 66, 67, 69 and 70, specific to control cytosines, and their combinations.
  • the oligonucleotides used to analyze the methylation status of the cytosines that constitute the WNT-SHH Panel by pyrosequencing methodology are biotinylated hybridization primers and / or probes for the cytosines of interest (problem cytosines and control cytosines of DNA conversion with bisulfite).
  • the oligonucleotides employed are selected from those that have the sequences shown in SEQ ID 1-6, 9-14, 17, 18 and 35-47, specific for the problem cytosines, and SEQ ID NO 48-71, specific for control cytosines, and their combinations.
  • kits for carrying out the in vitro method for the classification of a patient with medulloblastoma in one of the molecular subgroups WNT, SHH, group 3 and group 4 comprising:
  • the oligonucleotide set used to analyze the methylation status of the cytosines that constitute the WNT-SHH Panel and the G3-G4 Panel using BSP methodology are primers for sequencing, specific for the cytosines of interest (problem cytosines and cytosines for the control of the efficiency of the reaction of conversion of DNA with sodium bisulfite).
  • the oligonucleotides used are selected from those that have sequences shown in SEQ ID NO 1-34, the oligonucleotides of SEQ ID NO 1-18 sequences specific for the problem cytosines of the WNT-SHH panel, and the sequence oligonucleotides.
  • SEQ ID NO 19-34 specific to the G3-G4 panel problem cytosines, and sequence oligonucleotides SEQ ID No 48, 49, 51, 52, 54, 55, 57, 58, 60, 61, 63, 64, 66, 67, 69 and 70, specific for control cytosines, and combinations thereof.
  • the set of oligonucleotides used to analyze the state of methylation of the cytosines that constitute Panel WNT-SHH and Panel G3-G4 by pyrosequencing methodology are primers and / or probes of biotinylated hybridization specific for the cytosines of interest (problem cytosines and control cytosines of DNA conversion with bisulfite).
  • the oligonucleotides employed are selected from those having sequences shown in SEQ ID NO 1-6, 9-14, 17-47 and 72-79, the oligonucleotides of sequences SEQ ID NO 1-6, 9-14 being , 17, 18, 35-47, specific for the problem cytosines of the WNT-SHH panel and the sequence oligonucleotides SEQ ID NO
  • the kit of the present invention includes: oligonucleotides of specific type for the methodology employed, for the combination of cytosines of Panel WNT-SHH and / or Panel G3-G4, oligonucleotides specific for cytosines reference positive control / negative, a master mix containing a thermostable Taq polymerase, a suitable buffer and MgC at optimal concentrations, in addition to the optimized dNTPs for the methodology.
  • the present invention contemplates the set of oligonucleotides, of sequences SEQ ID NO 1-79, designed for use in the analysis of the levels of methylation of the cytosines of the WNT-SHH panel and / or
  • the study was based on the hypothesis that there are differential methylation patterns between the molecular subgroups of medulloblastoma (MB) with clear differences in clinical or biologically distinct behavior, and that these methylation profiles are likely to represent a molecular classification marker in patients with MB.
  • MB medulloblastoma
  • DNA methylation data were obtained using high density microarray technology (Human Methylation BeadChip 450K, HM450K). These methylation data were generated in the context of genomic studies that have identified and described the presence of four major molecular subgroups of MB: wingless (WNT), Sonic hedgehog (SHH), Group 3 and Group 4.
  • WNT Wingless
  • SHH Sonic hedgehog
  • GSE44 from pilocytic astrocytoma
  • the study was based on raw genomic data (files called Intensity Data files - ⁇ Dat) included in the GSE54880 database, generated from a total of 106 primary medulloblastomas (study cohort) obtained fresh at the time of diagnosis (Table 5). From the Dat files of the study cohort, a single database was generated. Next, we proceeded with the normalization, quality control and filtering of the methylation data, as previously described (Gómez S et al. DNA methylation fingerprint of neuroblastoma reveal new biological! And clinical insights. Genomics
  • ChAMP 450k Chip Analysis Methylation Pipeline. Bioinformatics 2014, 30 (3): 428-430; Morris TJ et al. The ChAMP Package (2016) Human Methylation EPIC Analysis www.bioconductor.org/packages/devel/bioc/vignettes/ChAMP/inst/'doc/ChAMP.pdf); Butcher LM and Beck S Probe Lasso: A novel method to rope in differentially methylated regions with 450K DNA methylation data. Methods 2015, 72, pp. 21-28. Doi: 10. 1016) among others ( Figure 1 C).
  • Cytosines with detection values with a P value> 0.01 in more than 10% of the samples, as well as those methylation data associated with the sex-specific methylation imprint, were excluded from the initial database (485,512 cytosines for each sample). The remaining values (n 475.038 CpG) constituted the starting database for the study.
  • DNA methylation patterns were analyzed using the unsupervised multivariate statistical method called Principal Component Analysis (ACP).
  • ACP Principal Component Analysis
  • Principal Component Analysis is a linear mathematical technique of information synthesis, or reduction of the size of a data set (number of variables). That is, before a database with many variables (in this case, lists of cytosines with different states of methylation), the objective will be to reduce them to a smaller number by losing as little information as possible.
  • the new main components or factors will be a linear combination of the original variables, and will also be independent of each other.
  • a key aspect in ACP is the interpretation of the factors, since this is not given a priori, but will be deduced after observing the relationship of the factors with the initial variables. As it is a non-supervised statistical method, the ACP does not take into account the clinical variables.
  • Unsupervised clustering is a set of techniques that regroup data based on a distance without using any external information to organize the groups.
  • Hierarchical clustering is a method based on a distance matrix. It establishes groups of conditions that have a common / similar pattern and constructs a dendrogram (graphical representation of a group of relationships based on the proximity or similarity of the data). The dendrogram establishes an ordered relationship of the previously defined groups and the length of its branches is a representation of the distance between the different nodes of the same.
  • the authors of the invention then delved into the analysis of the methylation data of the 5,904 most significant cytosines, in order to profile the differential methylation patterns and reduce them to a lower number of cytosines, losing as little information as possible.
  • the main objective was to reduce the redundant DNA methylation information to identify a methylation pattern composed of few factors (cytosines) that will explain much of the total variability of the MB.
  • cytosines From the methylation patterns of the 5,904 cytosines, two sets of cytosines were identified that met the desired selection criteria. From these methylation patterns, a first panel of nine differentially methylated cytosines was selected, with a differential methylation pattern that was significantly and accurately associated with each of the molecular subgroups defined according to the tumor classification of the WHO central nervous system (2016): WNT, SHH and Non-WNT / non-SHH Group.
  • Each subgroup was specifically and univocally associated with a differential methylation pattern of the cytosines of the WNT-SHH Panel.
  • Each cytosine showed a specific bimodal methylation pattern (very high levels of methylation; average methylation value ⁇ 80%), or conversely, very low levels; average methylation value ⁇ 17%) for each of the subgroups, as shown in Table 7 and Figure 3B.
  • LDA Linear Discriminant Analysis
  • the methylation values of the nine cytosines of the WNT-SHH Panel of the study cohort were used to train the LDA function and generate an LDA classification model.
  • the LDA function was also applied to test all possible combinations (2 9 combinations) to define which cytosines and how many of these were necessary to obtain the best possible classification.
  • Both the nine cytosines and all possible combinations (2 9 combinations) allowed to classify the totality of the samples of the cohort and a concordance of 100% was observed between the classification made with the various combinations of the WNT-SHH Panel and the previously published data with the same cohort of MB.
  • Panel G3-G4 is capable of representing a useful marker for the classification of MBs belonging to the genetic entities Group 3 and Group 4 (Table 8).
  • cytosines Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB they are specifically and uniquely associated with the subgroup Group 3 of MBs. While low values in cytosines Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB is an indicator of a tumor belonging to the Group 4 subgroup ( Table 8 and Figure 4B).
  • Panel G3-G4 showed a high specificity of the methylation pattern of the eight cytosines in MB compared to methylation values in other normal human tumors and tissue (Figure 4C).
  • FFPE paraffin
  • DNA methylation data was obtained using high density microarray technology ⁇ Illuminates HumanMethylation BeadChip450k, HM450K). These methylation data were generated in the context of previously published genomic studies (Hovestadt V et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archives! Tumor material using high-density DNA methylation arrays.
  • the molecular subgroup of the validation cohort was determined by applying the LDA classification model. It was observed how the cytosines clearly discriminated and were able to classify all the samples with 100% concordance with the previously published classification data with the same MB cohort (Hovestadt V et al. Robust molecular subgrouping and copy- number profiling of medulloblastoma from small amounts of archival! tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125: 913-916).
  • cytosines of the validation databases corresponding to Panel G3-G4 were analyzed. These cytosines showed a methylation pattern equivalent to the pattern of the study cohort of Panel G3-G4, significantly and specifically associated with the subgroups Group 3 and Group 4 ( Figure 5B and Table 10).
  • the objective of the study was to validate the analysis of the cytosines of interest (Panel WNT-SHH and Panel G3-G4) through molecular techniques such as bisulfite sequencing (BSP) and bisulfite pyrosequencing, or other techniques Similar molecular molecules suitable for analyzing the DNA methylation status.
  • BSP bisulfite sequencing
  • BSP bisulfite pyrosequencing
  • the ultimate goal was to demonstrate that the primary MB classification method can be analyzed by various molecular methodologies and applicable to various types of tissue.
  • a biological sample was isolated from a patient.
  • the DNA was extracted using conventional protocols, DNA treatment and subsequent analysis of the levels of methylation of each of the cytosine of interest.
  • Group 1 96 MB frozen at -80 ° C (21 WNT, 26 SHH, 26 Group 3 and 23 Group 4)
  • Group 2 12 MB FFPE (2 WNT, 2 SHH, 6 Group 3 and 2 Group 4)
  • DNA was extracted from fresh frozen samples using the Gentra Puregene Tissue kit (Qiagen Technologies) or similar, following the manufacturer's instructions. DNA quantification was performed by absorbance reading at 260nm wavelength, in a spectrophotometer (Nanodrop N-1000, Thermo Scientific) or similar. The DNA purity was evaluated by 260nm absorbance and the absorbance coefficient at 260/280 nm, considering the optimal values between 1, 6 -1, 9 units of optical density (D.O.).
  • the initial step of the molecular techniques used to analyze the state of methylation is the conversion of DNA with sodium bisulfite (NaHSOs).
  • NaHSOs sodium bisulfite
  • the starting point was 1 ng - 2 ⁇ g of DNA and the DNA was converted using the EpiTect Plus Bisulfite Conversion kit (Qiagen Technologies) or similar, following the supplier's instructions (Table 12 and Table 13). See also detailed description of the methodology in the author's article Darst RP et al. Bisulfite Sequencing of DNA. Current Protocols of Molecular Biology (2010) doi: 10. 1002/0471142727. MB0709S91.
  • PCR polymerase chain reaction
  • the following conditions of the thermal cycler were applied: initial denaturation at 95 ° C, 5 minutes (35 cycles); denaturation at 95 ° C, 15 seconds, banding at appropriate temperature (banding temperatures vary according to the fragment to be analyzed, Tables 14, 15 and 16), 15 seconds, extension at 72 ° C, 30 seconds, final extension at 72 ° C , 7 minutes. In the end the machine was programmed to keep the tubes at 4 ° C (standby mode). Finally, the samples were electrophoresed with 2% agarose gel. The times and number of cycles of the PCR reaction may vary according to optimization.
  • sequence reaction was then performed using the same primers used for the PCR amplification reaction (Table 18).
  • the Forward (Fw) and Reverse (Rv) primers were used separately for methylated and nonmethylated alleles.
  • Fw Forward
  • Rv Reverse
  • the tubes were placed in a thermocycler and proceeded according to the following temperature and cycle conditions: initial denaturation of the tempered DNA at 96 ° C, 1 minute. Then, 25 cycles of denaturation at 96 ° C, 10 seconds, banding at 50 ° C, 5 seconds, extension at 60 ° C, 4 minutes.
  • sequence reaction product was precipitated by Sephadex G-50® (GE Healthcare Life Science) or the like, following the instructions of the supplier.
  • 10 ⁇ of the sequencing product was added to the AutoSeqTM G-50 ⁇ column of the kit (GE).
  • N-WS1_MB cg 18849583 (SEQ ID NO 36)
  • N-WS2_MB cg 19828869 (SEQ ID NO 10)
  • N-WS3_MB cg01268345 (SEQ ID NO 12)
  • Table 22 Composition of the reagent mixture for the amplification of a bisulfite converted DNA region for pyrosequencing.
  • Primer A / Primer B Variable / variab 0.2 ⁇ / 0.2 ⁇
  • the PCR tubes were introduced into the thermal cycler and proceeded according to the conditions described in Table 23, standard protocol subject to optimization.
  • the master sample was prepared with the "Streptavidin Sepharose High Performance" microspheres and DNA immobilization reagents according to the data in Table 24. 70 ⁇ of master mix was added to each well of a PCR plate next to 10 ⁇ of biotinylated PCR (total volume per well 80 ⁇ ) and the plate was centrifuged (1,400rpm) for 5-10 minutes, according to standard protocol subject to optimization.
  • the samples were prepared prior to the pyrosequencing analysis in PyroMark Q24 (Qiagen) or similar.
  • PyroMark Q24 plate Qiagen
  • 40 ⁇ of alliniament buffer and 0.5 ⁇ of specific primer for the PCR products were added in each well.
  • the PyroMark Q96 P ⁇ ate Low plate was positioned in the corresponding place in the vacuum station (Qiagen) or similar.
  • the PCR plate was placed in the corresponding position in the vacuum station.
  • Vacuum probes were introduced into the PCR plate to capture the microspheres with immobilized PCR products. After a series of washes, the microspheres were released on the PyroMark Q24 plate, following the manufacturer's recommendations (PyroMark Q24 User Manual, Qiagen). Finally, the plate was heated to 85 ° C, 2 minutes.
  • reagents were loaded into the PyroMark Q24 cartridge (Qiagen) or similar, and positioning of said reagent in the PyroMark Q24 system.
  • Reagents include a mixture of enzymes, a mixture of substrates and nucleotides (A, T, G, C), according to the manufacturer's recommendations
  • Table 25 Example of the classification capacity of the WNT-SHH Panel in tumor tissue in F / FF.
  • Table 26 Example of the classification capacity of the WNT-SHH Panel in tumor tissue in FFPE.
  • the left column represents the molecular classification according to published data ((Northcott PA et al. Medulloblastoma Comprises Four Distinct Molecular Variants. Journal of Clinical Oncology 2011, 29 (11): 1408-1414)).
  • the affiliation according to the pyrosequencing results and in the right the classification according to the WNT-SHH panel.
  • Table 28 Example of the classification capacity of Panel G3-G4 in tumor tissue in FFPE.
  • the pattern of differential methylation of the cytosines that constitute Panel WNT-SHH and Panel G3-G4 represents an effective marker for the molecular classification of MBs belonging to the genetic entities WNT, SHH, Group 3 and Group 4.
  • the methylation profile of the cytosines of interest of tumor tissue fixed in formalin and included in paraffin is comparable to tissue obtained in fresh and / or preserved frozen at -80 ° C. Therefore, by keeping the cytosine methylation pattern of the proposed markers, Panel WNT-SHH and Panel G3-G4, stable, it was found that the proposed classification method is applicable to this type of biological material.

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Abstract

The present invention relates to use of the combination of the cytosine methylation profiles from the WNT-SHH panel and the G3-G4 panel as a marker for classifying medulloblastoma patients into the WNT, SHH, Group 3 and Group 4 sub-groups. The invention also relates to the method for classifying medulloblastoma patients into one of said molecular sub-groups based on the cytosine methylation analysis in the WNT-SHH and G3-G4 panels. Finally, the invention relates to the kit for carrying out the classification method of the invention.

Description

MÉTODO DE CLASIFICACIÓN EN SUBGRUPOS MOLECULARES DE CLASSIFICATION METHOD IN MOLECULAR SUBGROUPS OF
PACIENTES CON MEDULOBLASTOMA DESCRIPCIÓN PATIENTS WITH MEDULOBLASTOMA DESCRIPTION
Campo de la invención Field of the Invention
La presente invención tiene su campo de aplicación dentro del sector sanitario, principalmente en los sectores de la Oncología Pediátrica" y la "Biología Molecular". En particular, la presente invención se refiere a un método de clasificación de tumores en subgrupos moleculares de interés clínico. De forma más específica, la presente invención contempla un método in vitro para la clasificación de un paciente con meduloblastoma en uno de los subgrupos moleculares WNT, SHH, Grupo 3 y Grupo 4. Antecedentes de la invención The present invention has its field of application within the health sector, mainly in the Pediatric Oncology "and" Molecular Biology "sectors. In particular, the present invention relates to a method of classifying tumors into molecular subgroups of clinical interest. More specifically, the present invention contemplates an in vitro method for the classification of a patient with medulloblastoma in one of the molecular subgroups WNT, SHH, Group 3 and Group 4. Background of the invention
El meduloblastoma (MB) es un tumor embrionario altamente maligno de origen neuroepitelial que se describió por primera vez como tumor del sistema nervioso central en 1925. Es el tumor cerebral maligno más común en la edad pediátrica y representa aproximadamente el 20% de los tumores pediátricos de sistema nervioso central (Gilbertson RJ and Ellison DW. The Origins of Medulloblastoma Medulloblastoma (MB) is a highly malignant embryonic tumor of neuroepithelial origin that was first described as a tumor of the central nervous system in 1925. It is the most common malignant brain tumor in the pediatric age and represents approximately 20% of pediatric tumors of the central nervous system (Gilbertson RJ and Ellison DW. The Origins of Medulloblastoma
Subtypes. Annual Review of Pathology Mechanisms of Disease 2008, 3:341-365; Gajiar A et al. Pediatric Brain Tumors: Innovative Genomic Information is transforming the Diagnostic and Clinical Landscape. Journal of Clinical Oncology 2015, 33(7). -2986-2988). En los países desarrollados, el MB es la causa más común de muerte por cáncer en niños mayores de 1 año de edad (European detailed mortality datábase (DMDB). Copenhagen, WHO Regional Office of Europe, 2015). La edad de presentación de la enfermedad es variable, y puede desarrollarse tanto en niños pequeños como en adolescentes, con un pico de incidencia en niños de 3 a 6 años de edad. En jóvenes adultos es raro. Aproximadamente un 75% de los MBs de la edad pediátrica tienen origen en el vermis cerebeloso, y protruyen hacia el cuarto ventrículo, con el restante 25% localizado en los hemisferios cerebelosos. Alrededor de un 30% de los casos pediátricos presentan metástasis en el momento del diagnóstico. La mayoría de las metástasis se desarrollan en el sistema nervioso central (craneal o medular), mientras que la propagación a los órganos extracraneales es muy rara al diagnóstico. En una minoría de pacientes, el MB está asociado con el síndrome de Gorlin (www.orpha.net, ORPHA377), la poliposis adenomatosa o con el síndrome de Li-Fraumeni (www.orpha.net; ORPHA616). Subtypes Annual Review of Pathology Mechanisms of Disease 2008, 3: 341-365; Gajiar A et al. Pediatric Brain Tumors: Innovative Genomic Information is transforming the Diagnostic and Clinical Landscape. Journal of Clinical Oncology 2015, 33 (7). -2986-2988). In developed countries, MB is the most common cause of cancer death in children over 1 year of age (European detailed mortality data base (DMDB). Copenhagen, WHO Regional Office of Europe, 2015). The age of presentation of the disease is variable, and can develop in both young children and adolescents, with a peak incidence in children 3 to 6 years of age. In young adults it is rare. Approximately 75% of MBs of pediatric age originate in the cerebellar vermis, and protrude into the fourth ventricle, with the remaining 25% located in the cerebellar hemispheres. About 30% of pediatric cases have metastases at the time of diagnosis. most of Metastases develop in the central nervous system (cranial or medullary), while the spread to extracranial organs is very rare at diagnosis. In a minority of patients, MB is associated with Gorlin syndrome (www.orpha.net, ORPHA377), adenomatous polyposis or with Li-Fraumeni syndrome (www.orpha.net; ORPHA616).
El diagnostico diferencial del MB se plantea, macroscópicamente, con otros tumores de fosa posterior (vermis cerebeloso) como el astrocitoma pilocítico y el ependimoma. El tumor teratoide rabdoide atípico también ha de barajarse en determinadas situaciones. Desde el punto de vista microscópico, la confusión con el astrocitoma pilocítico es rara, con excepción de los tumores desdiferenciados, como astrocitomas malignizados o glioblastomas de esta región. Con respecto a los ependimomas, la dificultad puede plantease en los casos de elevada densidad celular, en los que los sistemas gliovasculares pequeños pueden inducir a confusión con rosetas o pseudorosetas y con otras estructuras secundarias características de los ependimomas (Gilbertson RJ and Ellison DW. The Origins of The differential diagnosis of MB is posed, macroscopically, with other tumors of the posterior fossa (cerebellar vermis) such as pilocytic astrocytoma and ependymoma. Atypical rhabdoid teratoid tumor must also be considered in certain situations. From a microscopic point of view, confusion with pyrocytic astrocytoma is rare, with the exception of dedifferentiated tumors, such as malignant astrocytomas or glioblastomas of this region. With regard to ependymomas, the difficulty may arise in cases of high cell density, in which small gliovascular systems can induce confusion with rosettes or pseudo-soils and with other secondary structures characteristic of ependymomas (Gilbertson RJ and Ellison DW. Origins of
Medulloblastoma Subtypes. Annual Review of Pathology Mechanisms of Disease 2008, 3:341-365; Escalona-Zapata J. Tumores del Sistema Nervioso Central. Editorial Complutense 1996). El tratamiento del MB depende de la edad, la diseminación de la enfermedad, la variante histológica y las características moleculares del tumor. En la actualidad el tratamiento para MB incluye resección quirúrgica, radioterapia craneoespinal (en pacientes mayores de 3 años) y quimioterapia adyuvante. El tratamiento multimodal intensivo de los MBs ha permitido mejorar de forma significativa la supervivencia de los pacientes, supervivencia global a 5 años de un 80% en los casos con enfermedad de riesgo estándar y un 70% en casos de alto riesgo clínico, aunque en muchas ocasiones a expensas de secuelas severas permanentes (alteraciones del desarrollo, secuelas neurológicas, neuroendocrinas y psicosociales) (Gajjar A et al. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape. Journal ofMedulloblastoma Subtypes. Annual Review of Pathology Mechanisms of Disease 2008, 3: 341-365; Escalona-Zapata J. Tumors of the Central Nervous System. Editorial Complutense 1996). The treatment of MB depends on the age, the spread of the disease, the histological variant and the molecular characteristics of the tumor. Currently, treatment for MB includes surgical resection, craniospinal radiotherapy (in patients older than 3 years) and adjuvant chemotherapy. The intensive multimodal treatment of MBs has significantly improved patient survival, 5-year overall survival of 80% in cases with standard risk disease and 70% in cases of high clinical risk, although in many occasions at the expense of permanent severe sequelae (developmental disorders, neurological, neuroendocrine and psychosocial sequelae) (Gajjar A et al. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape. Journal of
Clinical Oncology 2015, 33(27): 2986-2998; Northcott PA. et al. Medulloblastomics: the end of the beginning. Nature Reviews Cáncer 2012, 12: 818-834). El MB representa un grupo heterogéneo de tumores del cerebelo caracterizados por tener comportamiento clínico, histopatología, biología e índices de curación diversos (Gajjar A et al. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape. Journal of Clinical Oncology 2015; 33(27): 2986-2998). Los esquemas de estratificación clínica utilizados durante décadas se han basado únicamente en características clínicas (tamaño y estado de diseminación del tumor), en la edad al diagnóstico, el grado de resección quirúrgica y la histología del tumor. La edad del paciente al momento del diagnóstico es un factor determinante al verse reflejada en el comportamiento agresivo de los tumores en pacientes menores de 3 años (DeSouza RMet al.Clinical Oncology 2015, 33 (27): 2986-2998; Northcott PA. et al. Medulloblastomics: the end of the beginning. Nature Reviews Cancer 2012, 12: 818-834). The MB represents a heterogeneous group of cerebellar tumors characterized by having diverse clinical behavior, histopathology, biology and cure rates (Gajjar A et al. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape. Journal of Clinical Oncology 2015 ; 33 (27): 2986-2998). The clinical stratification schemes used for decades have been based solely on clinical characteristics (size and state of dissemination of the tumor), on the age at diagnosis, the degree of surgical resection and the histology of the tumor. The age of the patient at the time of diagnosis is a determining factor as it is reflected in the aggressive behavior of tumors in patients younger than 3 years (DeSouza RMet al.
Pedriatic medulloblastoma - update on molecular classification driving targeted therapies. Frontiers in Oncology 2014, 4:1-8). Pacientes menores de 3 años de edad, con evidencia de tumor residual (≥1.5cm2) después de la cirugía y pacientes con diseminación leptomeníngea al diagnóstico se consideran tumores de alto riesgo, el resto de MB se clasifican de riesgo estándar (Northcott PA. et al.Pedriatic medulloblastoma - update on molecular classification driving targeted therapies. Frontiers in Oncology 2014, 4: 1-8). Patients under 3 years of age, with evidence of residual tumor (≥1.5cm 2 ) after surgery and patients with leptomeningeal dissemination at diagnosis are considered high-risk tumors, the rest of MB are classified as standard risk (Northcott PA. et al.
Medulloblastomics: the end of the beginning. Nature Reviews Cáncer 2012, 12: 818-834). Este esquema de estratificación de pacientes no tiene en cuenta la heterogeneidad del comportamiento clínico de estos tumores. Los MBs son considerados altamente malignos, corresponden a un grado IV de la clasificación de los tumores del sistema nervioso central de la Organización Mundial de la Salud (OMS). De acuerdo a la clasificación de la OMS del 2007, histológicamente los MBs se clasifican en tres grandes grupos que incluyen el subtipo clásico, el meduloblastoma desmoplásico/nodular (MBEN) y el subtipo de células gigantes/anaplásico (LCA). El subtipo clásico es el más frecuente (66%), se caracteriza por estar compuesto de células pequeñas indiferenciadas, empaquetadas de forma densa, que se tiñen de azul con hematoxilina (basofílicas) con núcleos hipercromáticos y escaso citoplasma que frecuentemente forman rosetas de Homer-Wright. El MB clásico crece desde la parte más central del cerebelo (vermis cerebeloso) (Louis DN et al. WHO Classification of Tumors of theMedulloblastomics: the end of the beginning. Nature Reviews Cancer 2012, 12: 818-834). This patient stratification scheme does not take into account the heterogeneity of the clinical behavior of these tumors. MBs are considered highly malignant, correspond to a grade IV classification of tumors of the central nervous system of the World Health Organization (WHO). According to the 2007 WHO classification, histologically, MBs are classified into three large groups that include the classic subtype, desmoplastic / nodular medulloblastoma (MBEN) and the giant / anaplastic cell subtype (ACL). The classic subtype is the most frequent (66%), characterized by being composed of undifferentiated small cells, densely packed, which are stained blue with hematoxylin (basophilic) with hyperchromatic nuclei and sparse cytoplasm that frequently form homer rosettes. Wright. Classic MB grows from the most central part of the cerebellum (cerebellar vermis) (Louis DN et al. WHO Classification of Tumors of the
Central Nervous System. Lyon: IARC 2007; Ellison DW et al. Medulloblastoma. Pathology and genetics of tumors of the nervous system. World Health Organization Classification of Tumours. Lyon: IARC 2000). El meduloblastoma desmoplásico/nodular (MBEN) (15%) tiene un pronóstico más favorable al ser menos agresivo. Se desarrolla en la parte más lateral del hemisferio cerebeloso. El MB desmoplásico también presenta células pequeñas azules, pero con isletas pálidas libres de reticulina en un fondo rico en estroma y reticulina. El subtipo anaplásico (15%) se caracteriza por un marcado pleomorfismo nuclear y amoldamiento celular, y la variante de célula grande (2-4%) presenta población celular monomorfa con nucléolo prominente. Ambas variantes se caracterizan por una elevada proliferación celular, abundante apoptosis y pronóstico más desfavorable (Louis DN et al. WHO Classification of Tumors of the Central Nervous System. Lyon: IARC 2007). Central Nervous System Lyon: IARC 2007; Ellison DW et al. Medulloblastoma Pathology and genetics of tumors of the nervous system. World Health Organization Classification of Tumors. Lyon: IARC 2000). Medulloblastoma desmoplastic / nodular (MBEN) (15%) has a more favorable prognosis because it is less aggressive. It develops in the most lateral part of the cerebellar hemisphere. The desmoplastic MB also has small blue cells, but with pale islets free of reticulin on a background rich in stroma and reticulin. The anaplastic subtype (15%) is characterized by a marked nuclear pleomorphism and cellular molding, and the large cell variant (2-4%) has a monomorphic cell population with a prominent nucleolus. Both variants are characterized by high cell proliferation, abundant apoptosis and a more unfavorable prognosis (Louis DN et al. WHO Classification of Tumors of the Central Nervous System. Lyon: IARC 2007).
El comportamiento clínico-patológico del MB es un reflejo de las características genético-biológicas subyacentes del tumor. Durante los últimos años se han llevado a cabo varios estudios genómicos (secuenciación masiva del genoma, análisis del transcriptoma y del metiloma del ADN y estudio de las alteraciones cromosómicas) con el fin de definir las bases moleculares subyacentes al comportamiento clínico-patológico del MB. Uno de los hallazgos más relevantes derivado de estos estudios ha sido la identificación de cuatro subgrupos principales de MB denominados: wingless (WNT), Sonic hedgehog (SHH), Grupo 3 y Grupo 4. Estos subgrupos se caracterizan por tener un comportamiento clínico, un perfil transcripcional y una genética distinta (Northcott PA et al. Medulloblastoma Comprises Four Distinct Molecular Variants. Journal of Clinical Oncology 2011,29 (11) . 1408-1414; Kool M et al. Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas. Acta Neuropathologica 2012, 123: 473-484; Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123:465-472; Northcott PA et al Sugroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 2012, 488:49- 56; Northcott PA. et al. Medulloblastomics: the end of the beginning. NatureThe clinical-pathological behavior of MB is a reflection of the underlying genetic-biological characteristics of the tumor. In recent years, several genomic studies (massive genome sequencing, transcriptome and DNA methyloma analysis and study of chromosomal alterations) have been carried out in order to define the molecular basis underlying the clinical-pathological behavior of MB. One of the most relevant findings derived from these studies has been the identification of four main MB subgroups called: wingless (WNT), Sonic hedgehog (SHH), Group 3 and Group 4. These subgroups are characterized by having a clinical behavior, a Transcriptional profile and a different genetics (Northcott PA et al. Medulloblastoma Comprises Four Distinct Molecular Variants. Journal of Clinical Oncology 2011,29 (11). 1408-1414; Kool M et al. Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas Acta Neuropathologica 2012, 123: 473-484; Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123 : 465-472; Northcott PA et al Sugroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 2012, 488: 49-56; Northcott PA. Et al. Medulloblastomics: the end of the beginning. N ature
Reviews Cáncer 2012, 12: 818-834; Northcott PA et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathologica 2012, 123:615-626; Hovestadt V et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archiva! tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125:913-916; Wang X et al. Medulloblastoma subgroups remain stable across primary and metastatic compartments. Acta Neuropathologica 2015, 129:449-457; Thompson EM et al. Prognostic valué of medulloblastoma extent of resection after accounting for molecular subgroup: a retrospective integrated clinical and molecular analysis. The Lancet Oncology 2016; 17:485- 495). Los subgrupos WNT y SHH llevan el nombre de las vías de señalización celular que juegan un papel importante en la patogénesis del subgrupo. La biología del Grupo 3 y Grupo 4 es más desconocida, por consiguiente se han nombrado de forma genérica hasta que no se defina la biología subyacente a su comportamiento clínico (Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123:465-472). Cancer 2012, 12: 818-834; Northcott PA et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathologica 2012, 123: 615-626; Hovestadt V et al. Molecular robust subgrouping and copy-number profiling of medulloblastoma from small amounts of archiva! tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125: 913-916; Wang X et al. Medulloblastoma subgroups remain stable across primary and metastatic compartments. Acta Neuropathologica 2015, 129: 449-457; Thompson EM et al. Prognostic valué of medulloblastoma extent of resection after accounting for molecular subgroup: a retrospective integrated clinical and molecular analysis. The Lancet Oncology 2016; 17: 485-495). The WNT and SHH subgroups are named after cell signaling pathways that play an important role in the pathogenesis of the subgroup. The biology of Group 3 and Group 4 is more unknown, therefore they have been named generically until the underlying biology of their clinical behavior is defined (Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012 , 123: 465-472).
El subgrupo de MB más conocido es el subgrupo WNT debido a su pronóstico excelente en comparación con el resto de subgrupos. Los índices de supervivencia global de los MB WNT pueden superar el 90%, donde aquellos pacientes que fallecen es mayoritariamente debido a complicaciones asociadas al tratamiento o neoplasias secundarias (Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123:465-472). Este subgrupo representa aproximadamente el 10% de los pacientes con MB. Normalmente afecta a pacientes mayores (media de edad 10 años), la mayoría presentan una histología clásica y rara vez presentan metástasis. El 80-85% se asocian a presencia de monosomía del cromosoma 6 (Gajjar A et al. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape. Journal of Clinical Oncology 2015, 33(27): 2986-2998. El gen más recurrentemente mutado en los MBs WNT es CTNNB1 (del inglés catenin beta 1), que se identifica en un 85% de los tumores analizados. Mutaciones en el gen DDX3 se ven enriquecidas en el subgrupo WNT, pero el subgrupo SHH y Grupo 3 también pueden presentar estas mutaciones. Aproximadamente un 15% de los tumores WNT tienen mutaciones en el gen TP53 no asociadas con el síndrome de Li-Fraumeni (mutación en TP53 en línea germinal) o con pronóstico desfavorable (Gajjar A et al. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape. Journal of Clinical Oncology 2015, 33(27): 2986-2998; Taylor MD et al.The best known subgroup of MB is the WNT subgroup due to its excellent prognosis compared to the other subgroups. The overall survival rates of WNT MBs can exceed 90%, where those patients who die are mostly due to complications associated with treatment or secondary malignancies (Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123: 465-472). This subgroup represents approximately 10% of patients with MB. It usually affects older patients (mean age 10 years), most have a classical histology and rarely present with metastases. 80-85% are associated with the presence of chromosome 6 monosomy (Gajjar A et al. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape. Journal of Clinical Oncology 2015, 33 (27): 2986-2998. The most recurrently mutated gene in the WNT MBs is CTNNB1 (in English catenin beta 1), which is identified in 85% of the tumors analyzed. Mutations in the DDX3 gene are enriched in the WNT subgroup, but the SHH and Group subgroup 3 can also present these mutations. Approximately 15% of WNT tumors have mutations in the TP53 gene not associated with Li-Fraumeni syndrome (germline TP53 mutation) or with an unfavorable prognosis (Gajjar A et al. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape, Journal of Clinical Oncology 2015, 33 (27): 2986-2998; Taylor MD et al.
Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123:465-472). Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123: 465-472).
El subgrupo SHH representa aproximadamente el 25% de los MBs, mayoritariamente de histología desmoplásica nodular. El pronóstico es bastante variable y dependiente de la edad del paciente: niños pequeños con SHH tratados exclusivamente con quimioterapia tienen pronóstico excelente, mientras pacientes mayores con SHH asociado a mutaciones en TP53 tienen pronóstico desfavorable, especialmente si presentan amplificación de los genes MYCN y GLI2. The SHH subgroup represents approximately 25% of MBs, mostly of nodular desmoplastic histology. The prognosis is quite variable and dependent on the patient's age: young children with SHH treated exclusively with chemotherapy have an excellent prognosis, while older patients with SHH associated with mutations in TP53 have an unfavorable prognosis, especially if they have amplification of the MYCN and GLI2 genes.
El Grupo 3 y Grupo 4 constituyen el 25% y 35% de los MBs, respectivamente. Aun siendo genéticamente distintos, estos dos subgrupos presentan numerosas alteraciones genéticas comunes. Hasta la fecha, no se ha hallado un patrón específico que los distinga en Grupo 3 y Grupo 4 de forma concluyente. Ambos grupos son más frecuentes en niños, y el isocromosoma 17q sólo se presenta en estos tumores, siendo más frecuente en el Grupo 4 (80% vs. 26%). Los MBs del Grupo 3 y Grupo 4 muestran una frecuencia baja de mutaciones recurrentes. Se ha descrito la activación de la expresión de la familia de proto-oncogenes GFI1 y GFI1B mediante un mecanismo de "hijacking" (secuestro) de "enhancers" (potenciadores). Esta alteración genética GFI1/GFI1B está activa en aproximadamente un 40% y 10% de los tumores del Grupo 3 y Grupo 4, respectivamente. Los MBs Grupo 3 se asocian a histología LCA y diseminación metastásica (50%). También se caracterizan por sobre-expresión de MYC (17% presenta amplificación de MYC). La presencia de enfermedad metastásica, isocromosoma 17q y amplificación de MYC confiere al Grupo 3 un pronóstico desfavorable. Los tumores del Grupo 4 habitualmente poseen una histología clásica, y en ocasiones histología LCA. Los pacientes tienen un pronóstico intermedio. La amplificación del oncogén MYCN en estos tumores, a diferencia de los SHH, no se asocia a pronóstico desfavorable. Pacientes con enfermedad metastásica tienen mayor riesgo de recaída, excepto si el tumor pierde el cromosoma 1 1 y gana el cromosoma 17, que parece identificar un subgrupo de pronóstico más favorable (Gajjar A et al. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape.Group 3 and Group 4 constitute 25% and 35% of the MBs, respectively. Even though they are genetically distinct, these two subgroups have numerous common genetic alterations. To date, no specific pattern has been found that distinguishes them in Group 3 and Group 4 conclusively. Both groups are more frequent in children, and isochromosome 17q only occurs in these tumors, being more frequent in Group 4 (80% vs. 26%). The MBs of Group 3 and Group 4 show a low frequency of recurrent mutations. The activation of the expression of the proto-oncogenes family GFI1 and GFI1B by means of a "hijacking" mechanism of "enhancers" (enhancers) has been described. This GFI1 / GFI1B genetic alteration is active in approximately 40% and 10% of Group 3 and Group 4 tumors, respectively. Group 3 MBs are associated with ACL histology and metastatic dissemination (50%). They are also characterized by MYC overexpression (17% show MYC amplification). The presence of metastatic disease, isochromosome 17q and amplification of MYC gives Group 3 an unfavorable prognosis. Group 4 tumors usually have a classical histology, and sometimes ACL histology. Patients have an intermediate prognosis. The amplification of the MYCN oncogene in these tumors, unlike SHH is not associated with an unfavorable prognosis. Patients with metastatic disease have a higher risk of relapse, except if the tumor loses chromosome 1 1 and gains chromosome 17, which seems to identify a more favorable prognosis subgroup (Gajjar A et al. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming The Diagnostic and Clinical Landscape.
Journal of Clinical Oncology 2015, 33(27): 2986-2998; Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123:465-472). Los subgrupos WNT, SHH, Grupo 3 y Grupo 4 de MB han adquirido cada vez más relevancia tanto para definir de forma más precisa el pronóstico clínico o el tratamiento de los pacientes, como para el diseño de ensayos clínicos. Por ejemplo, dado el pronóstico favorable del subgrupo WNT, estos pacientes podrían beneficiarse de una reducción u omisión de la radioterapia o quimioterapia, limitando de esta forma los efectos neurológicos adversos y toxicidades. Por el contrario, pacientes Grupo 3 con pronóstico desfavorable podrían beneficiarse de una intensificación del tratamiento de primera línea. Journal of Clinical Oncology 2015, 33 (27): 2986-2998; Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123: 465-472). The subgroups WNT, SHH, Group 3 and Group 4 of MB have become increasingly relevant both to define more accurately the clinical prognosis or the treatment of patients, as well as for the design of clinical trials. For example, given the favorable prognosis of the WNT subgroup, these patients could benefit from a reduction or omission of radiotherapy or chemotherapy, thereby limiting adverse neurological effects and toxicities. In contrast, Group 3 patients with an unfavorable prognosis could benefit from an intensification of first-line treatment.
En 2010, en una conferencia consenso en Boston, el grupo de Trabajo del Meduloblastoma (Medulloblastoma Working Group) reconoció los subgrupos deIn 2010, at a consensus conference in Boston, the Medulloblastoma Working Group recognized the subgroups of
MB, WNT, SHH, Grupo 3 y Grupo 4, como entidades biológicas distintas. Actualmente, se están realizando grandes esfuerzos para desarrollar nuevas estrategias terapéuticas dirigidas a cada uno de estas entidades de MB. En 2015, la conferencia consenso de la OMS reconoció la importancia de estos grupos biológicos e introdujo las siguientes entidades definidas genéticamente en la última revisión de la clasificación de los tumores del SNC publicada en 2016: WNT, SHH- TP53 no mutado (TP53 wild-type); SHH-TP53 mutado, y Grupo no-WNT/no-SHH. El Grupo 3 y Grupo 4, al tener un cierto grado de similitud y al coincidir algunas de las características genéticas, han sido incluidos de forma provisional en el subgrupo de MBs no-WNT/no-SHH (Louis DN et al. WHO Classification ofMB, WNT, SHH, Group 3 and Group 4, as distinct biological entities. Currently, great efforts are being made to develop new therapeutic strategies aimed at each of these MB entities. In 2015, the WHO consensus conference recognized the importance of these biological groups and introduced the following genetically defined entities in the last revision of the CNS tumor classification published in 2016: WNT, SHH-TP53 not mutated (TP53 wild- type); SHH-TP53 mutated, and Group no-WNT / no-SHH. Group 3 and Group 4, having a certain degree of similarity and coinciding with some of the genetic characteristics, have been provisionally included in the subgroup of non-WNT / non-SHH MBs (Louis DN et al. WHO Classification of
Tumours of the Central Nervous System. WHO/IARC Classification of Tumours, 4th Edition Revised, Volume 1, WHO press-IARC; Louis DN et al. The 2016 World Health Organization Classification of Tumor of the Central Nervous System: a summary. Acta Neuropathologica 2016, 131(6):803-820; Ramaswamy V et al. Risk stratification of childhood medulloblastoma in the molecular era: the current consensus. Acta Neuropathologica 2016, 131:821-831). La metodología utilizada para definir el subgrupo molecular de MB ha cambiado a lo largo de los últimos años. Inicialmente se realizaba mediante un análisis de expresión génica basado en tecnología de microarray, para ello era necesario partir de muestras de tejido fresco congelado (Northcott PA et. Medulloblastoma comprises four distinct molecular variants. Journal of Clinical Oncology 2011, 29(11): 1408-1414). También existen métodos para el análisis de los niveles ARN en tejido fijado e incluido en parafina, sin embargo la precisión es inferior, especialmente cuando se trata de muestras más antiguas. También se ha propuesto utilizar como metodología alternativa un análisis de un panel de marcadores mediante técnicas de inmunohistoquímica (Northcott PA et. Medulloblastoma comprises four distinct molecular variants. Journal of ClinicalTumors of the Central Nervous System. WHO / IARC Classification of Tumors, 4th Edition Revised, Volume 1, WHO IARC press-; Louis DN et al. The 2016 World Health Organization Classification of Tumor of the Central Nervous System: a summary. Acta Neuropathologica 2016, 131 (6): 803-820; Ramaswamy V et al. Risk stratification of childhood medulloblastoma in the molecular era: the current consensus. Acta Neuropathologica 2016, 131: 821-831). The methodology used to define the molecular subgroup of MB has changed over the past few years. Initially it was carried out by means of a gene expression analysis based on microarray technology, for this it was necessary to start samples of frozen fresh tissue (Northcott PA et. Medulloblastoma comprising four distinct molecular variants. Journal of Clinical Oncology 2011, 29 (11): 1408 -1414). There are also methods for the analysis of RNA levels in fixed tissue and included in paraffin, however the accuracy is lower, especially when it comes to older samples. It has also been proposed to use an analysis of a panel of markers as an alternative methodology using immunohistochemical techniques (Northcott PA et. Medulloblastoma comprises four distinct molecular variants. Journal of Clinical
Oncology 2011, 29(1 1):1408-1414) pero se ha demostrado que es difícil de estandarizar en los laboratorios de neuropatología. Oncology 2011, 29 (1 1): 1408-1414) but it has been shown to be difficult to standardize in neuropathology laboratories.
Actualmente se utilizan dos estrategias para la clasificación de estos tumores: 1) tecnología basada en la cuantificación de niveles de ARN mediante el empleo del sistema NanoString nCounter System (NanoString Technologies, Inc.) y 2) tecnología de microarrays de alta densidad (/Ilumina Infinium Human Methylation 450K BeadChip array (HM450K)) para analizar el perfil de metilación del genoma completo (ADN) de los tumores. Two strategies are currently used for the classification of these tumors: 1) technology based on the quantification of RNA levels through the use of the NanoString nCounter System (NanoString Technologies, Inc.) and 2) high density microarray technology (/ Illuminates Infinium Human Methylation 450K BeadChip array (HM450K)) to analyze the complete genome (DNA) methylation profile of tumors.
La tecnología NanoString nCounter System está basada en un análisis no enzimático con sondas secuencia-específicas para la cuantificación digital de los niveles de múltiples ARN diana en una muestra. En 2012, el grupo de Taylor MD del Sick Children's Hospital de Toronto identificó 22 genes cuyos niveles de expresión permiten clasificar los MBs (Northcott PA et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathologica 2012, 123:615-626). El mismo grupo ha desarrollado un ensayo (una librería de sondas para la cuantificación de ARN) que permite cuantificar los niveles de expresión de los 22 genes mediante un analizador digital. Esta metodología es aplicable tanto a muestras de tejido fresco congelado como tejido fijado en formalina e incluido en parafina (Northcott PA et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathologica 2012, 123:615-626). El análisis mediante NanoString nCounter ha mostrado ser fiable y reproducible, pero el coste elevado del analizador digital ha limitado la aplicabilidad en la práctica clínica. Actualmente el ensayo no es comercial y el análisis de la firma de 22 genes está centralizado en el Sick Children's Hospital de Toronto, Canadá. NanoString nCounter System technology is based on a non-enzymatic analysis with sequence-specific probes for the digital quantification of the levels of multiple target RNAs in a sample. In 2012, the Taylor MD group at the Sick Children's Hospital in Toronto identified 22 genes whose levels of expression allow classifying MBs (Northcott PA et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathologica 2012, 123: 615-626). The same group has developed an assay (a library of probes for RNA quantification) that allows quantification of Expression levels of the 22 genes using a digital analyzer. This methodology is applicable to both samples of fresh frozen tissue and formalin-fixed and paraffin-embedded tissue (Northcott PA et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathologica 2012, 123: 615-626 ). NanoString nCounter analysis has proven reliable and reproducible, but the high cost of the digital analyzer has limited applicability in clinical practice. The trial is currently non-commercial and the analysis of the 22 gene signature is centralized at Sick Children's Hospital in Toronto, Canada.
El microarray HM450k interroga el estado de metilación de más de 450.000 citosinas a lo largo de todo el genoma. Cubre el 96% por ciento de las islas de dinucleótidos citosina-guanina (CpG) de todo el genoma, múltiples CpG "shores" (orillas) y CpGs aislados localizados en regiones tanto intragénicas como intergénicas. Esta metodología ha demostrado ser fiable para la clasificación de los MBs en subgrupos tanto con tejido fresco congelado como fijado en formalina e incluido en parafina. Sin embargo, la utilidad del HM450k en la práctica clínica se ve limitada por el número extremadamente elevado de datos que genera el microarray (más de 450.000 datos), la dificultad del procesamiento de los datos (i.e. control de calidad de la hibridación de los fragmentos de ADN al array, control de la robustez de la señal de dichas secuencias, corrección de la señal debida a hibridación no especifica que afecta la sensibilidad y especificidad del resultado, normalización de la señal específica para contener errores técnicos) y la dificultad del análisis masivo de grandes cantidades de datos de metilación que hacen necesario el empleo de tecnología computacional, de métodos de bioinformáticaThe HM450k microarray interrogates the methylation status of more than 450,000 cytosines throughout the entire genome. It covers 96% percent of the cytosine-guanine dinucleotide islands (CpG) of the entire genome, multiple "shores" CpG (shores) and isolated CpGs located in both intragenic and intergenic regions. This methodology has proven to be reliable for the classification of MBs in subgroups with both fresh frozen tissue and formalin fixed and included in paraffin. However, the usefulness of HM450k in clinical practice is limited by the extremely high number of data generated by the microarray (more than 450,000 data), the difficulty of data processing (ie quality control of fragment hybridization of DNA to the array, control of the robustness of the signal of said sequences, correction of the signal due to non-specific hybridization that affects the sensitivity and specificity of the result, normalization of the specific signal to contain technical errors) and the difficulty of mass analysis of large amounts of methylation data that necessitate the use of computational technology, bioinformatics methods
(programación y aplicación de algoritmos/programas para el análisis, la clasificación, "data mining" (minería de datos) y visualización de dato, entre otros) y estadísticos complejos, personal especializado y cantidades elevadas de tejido adecuado. Finalmente, la tecnología de microarray tiene un coste económico elevado. Por todo ello, esta estrategia de clasificación de los MBs mediante tecnología de microarrays HM450K está centralizada en el Germán Cáncer Center de Heidelberg, Alemania. En respuesta a la necesidad de aplicar el sistema de clasificación molecular de pacientes con MB en la práctica clínica, los autores de la invención, tras un intenso trabajo de investigación, han conseguido identificar dos patrones de metilación de un grupo muy reducido y específico de citosinas (Panel WNT-SHH y Panel G3-G4) con niveles de metilación significativamente asociados con los cuatro subgrupos moleculares de meduloblastoma descritos en literatura: WNT, SHH, Grupo 3 y Grupo 4. Los datos de metilación del ADN fueron obtenidos mediante tecnología de microarray de alta densidad (¡Ilumina Human Methylation BeadChip 450K, HM450K) (Northcott PA et al. Medulloblastoma Comprises Four Distinct Molecular Variants. Journal of Clinical Oncology 2011, 29(11):1408-1414; Kool M et al.(programming and application of algorithms / programs for analysis, classification, "data mining" and data visualization, among others) and complex statistics, specialized personnel and high amounts of suitable tissue. Finally, microarray technology has a high economic cost. Therefore, this classification strategy for MBs using HM450K microarray technology is centralized at the German Cancer Center in Heidelberg, Germany. In response to the need to apply the molecular classification system of patients with MB in clinical practice, the authors of the invention, after intensive research work, have managed to identify two methylation patterns of a very small and specific group of cytosines (Panel WNT-SHH and Panel G3-G4) with levels of methylation significantly associated with the four molecular subgroups of medulloblastoma described in literature: WNT, SHH, Group 3 and Group 4. DNA methylation data were obtained using microarray technology High density (Illuminates Human Methylation BeadChip 450K, HM450K) (Northcott PA et al. Medulloblastoma Comprises Four Distinct Molecular Variants. Journal of Clinical Oncology 2011, 29 (11): 1408-1414; Kool M et al.
Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas. Acta Neuropathologica 2012, 123: 473-484; Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123:465-472; Northcott PA et al Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 2012, 488:49- 56; Northcott PA. et al. Medulloblastomics: the end of the beginning. Nature Reviews Cáncer 2012, 12: 818-834; Northcott PA et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathologica 2012, 123:615-626; Hovestadt V et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archiva! tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125:913-916; Wang X et al. Medulloblastoma subgroups remain stable across primary and metastatic compartments. Acta Neuropathologica 2015, 129:449-457; Thompson EM et al. Prognostic valué of medulloblastoma extent of resection after accounting for molecular subgroup: a retrospective integrated clinical and molecular analysis. The Lancet Oncology 2016; 17:485- 495). El empleo del perfil de metilación de las citosinas del Panel WNT-SHH y del PanelMolecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas. Acta Neuropathologica 2012, 123: 473-484; Taylor MD et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathologica 2012, 123: 465-472; Northcott PA et al Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 2012, 488: 49-56; Northcott PA. et al. Medulloblastomics: the end of the beginning. Nature Reviews Cancer 2012, 12: 818-834; Northcott PA et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathologica 2012, 123: 615-626; Hovestadt V et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archiva! tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125: 913-916; Wang X et al. Medulloblastoma subgroups remain stable across primary and metastatic compartments. Acta Neuropathologica 2015, 129: 449-457; Thompson EM et al. Prognostic valué of medulloblastoma extent of resection after accounting for molecular subgroup: a retrospective integrated clinical and molecular analysis. The Lancet Oncology 2016; 17: 485-495). The use of the cytosine methylation profile of the WNT-SHH Panel and the Panel
G3-G4, de forma combinada, puede ser empleado como marcador para la estratificación en los cuatros principales subgrupos moleculares de pacientes con MB. La metilación del ADN es una modificación post-replicativa que implica la unión covalente de un grupo metilo [-CH3] al carbono de la posición 5 de citosinas que preceden guaninas (dinucleótidos citosina-guanina o CpG). Estos no se encuentran uniformemente distribuidos en el genoma humano, existen regiones donde su concentración es elevada denominadas "islas citosina-guanina" (¡CpG). La metilación del ADN es un proceso muy bien caracterizado. Cuando las células se dividen, además de heredar la secuencia de su genoma, heredan los patrones de metilación presentes en la célula de origen. A diferencia de la información genética, la cual es transmitida de células madres a hijas con una tasa de variación baja, la información epigenética presenta mayor dinámica. G3-G4, in combination, can be used as a marker for stratification in the four main molecular subgroups of patients with MB. DNA methylation is a post-replicative modification that involves the covalent attachment of a methyl group [-CH3] to the carbon 5 position of cytosines that precede guanines (cytosine-guanine dinucleotides or CpG). These are not uniformly distributed in the human genome, there are regions where their concentration is high called "cytosine-guanine islands" (CpG). DNA methylation is a very well characterized process. When cells divide, in addition to inheriting the sequence of their genome, they inherit the methylation patterns present in the cell of origin. Unlike genetic information, which is transmitted from stem cells to daughters with a low variation rate, epigenetic information presents greater dynamics.
La metilación del genoma en dinucleótidos CpG es un mecanismo epigenético de regulación génica implicado en procesos celulares primordiales para el desarrollo embrionario de los mamíferos (Smith ZD et al. DNA methylation: roles in mammalian development. Nat Rev Genet (2013) 14(3), 204-20). La epigenética comprende todos aquellos mecanismos celulares (metilación del ADN, modificaciones de las histonas o ARN no codificante) que influyen sobre la regulación genética, sin alterar la secuencia de los genes o del genoma. Los procesos epigenéticos constituyen una programación esencial para el desarrollo y la diferenciación. Durante el desarrollo, el genoma sufre modificaciones que son cruciales para la determinación del linaje celular y la diferenciación (Smith ZD et al. DNA methylation: roles in mammalian development. Nat Rev Genet (2013) 14(3), 204-20). Estas modificaciones ocurren de forma natural en la célula pero pueden verse moduladas por diversos factores como la edad, el medio ambiente y las enfermedades. Estudios recientes están poniendo de manifiesto el papel clave que juegan las alteraciones epigenéticas en enfermedades como el cáncer (Esteller M et al. Epigenetics in cáncer. New England Journal of Medicine (2008) 358:1148-59; Lister R et al. Human DNA methyiomes at base resolution show widespread epigenomic differences. Nature (2009) 462:315-22). Genome methylation in CpG dinucleotides is an epigenetic mechanism of gene regulation involved in primary cellular processes for embryonic development of mammals (Smith ZD et al. DNA methylation: roles in mammalian development. Nat Rev Genet (2013) 14 (3) , 204-20). Epigenetics comprises all those cellular mechanisms (DNA methylation, histone modifications or non-coding RNA) that influence genetic regulation, without altering the sequence of the genes or genome. Epigenetic processes constitute an essential programming for development and differentiation. During development, the genome undergoes modifications that are crucial for the determination of cell lineage and differentiation (Smith ZD et al. DNA methylation: roles in mammalian development. Nat Rev Genet (2013) 14 (3), 204-20). These modifications occur naturally in the cell but can be modulated by various factors such as age, environment and diseases. Recent studies are showing the key role that epigenetic alterations play in diseases such as cancer (Esteller M et al. Epigenetics in cancer. New England Journal of Medicine (2008) 358: 1148-59; Lister R et al. Human DNA methyiomes at base resolution show widespread epigenomic differences. Nature (2009) 462: 315-22).
La modificación epigenética más estudiada en humanos es la metilación del ADN. El análisis de la metilación ha experimentado una revolución durante la última década, especialmente desde la adaptación de la tecnología de microarray al estudio de la metilación y a la aparición de la secuenciación de nueva generación. Dado que la información de la metilación del ADN se borra tras la reacción en cadena de la polimerasa (PCR, del inglés Polymerase Chain Reaction) (debido a la ausencia de metiltransferasas que mantengan el patrón de metilación), la gran mayoría de técnicas se basan en un tratamiento metil-dependiente previo a la amplificación o hibridación (Lister R et al. Human DNA methyiomes at base resolution show widespread epigenomic differences. Nature (2009) 462:315-22; Laird PW et al. Principies and challenges of genome-wide DNA methylation analysis. Nature Reviews Genetics (2010) 11:191-203; Balaguer F et al. Epigenómica. Nuevos Métodos de diagnóstico Molecular (2010) 9(4): 165-71). The most studied epigenetic modification in humans is DNA methylation. The methylation analysis has undergone a revolution during the last decade, especially since the adaptation of microarray technology to the study of methylation and the emergence of new generation sequencing. Since DNA methylation information is deleted after the polymerase chain reaction (PCR) (due to the absence of methyltransferases that maintain the methylation pattern), the vast majority of techniques are based in a methyl-dependent treatment prior to amplification or hybridization (Lister R et al. Human DNA methyiomes at base resolution show widespread epigenomic differences. Nature (2009) 462: 315-22; Laird PW et al. Principles and challenges of genome- wide DNA methylation analysis Nature Reviews Genetics (2010) 11: 191-203; Balaguer F et al. Epigenomics New Molecular Diagnostic Methods (2010) 9 (4): 165-71).
Existen múltiples aproximaciones posibles para el análisis de la metilación, basadas en diferentes estrategias enzimáticas y químicas (tratamiento con bisulfito de sodio (NaHSOs), tratamiento con enzimas de restricción y enriquecimiento por afinidad, entre otras). There are multiple possible approaches for the analysis of methylation, based on different enzymatic and chemical strategies (treatment with sodium bisulfite (NaHSOs), treatment with restriction enzymes and affinity enrichment, among others).
El tratamiento con bisulfito de sodio (BS) convierte las citosinas no metiladas en uracilos, y timinas y por consiguiente, la gran mayoría del genoma se reduce a tres bases (A, G, y T) en lugar de cuatro. Por tanto, para analizar el patrón de metilación es necesario el diseño de ensayos específicos para el ADN convertido por BS. Este tratamiento convierte una modificación epigenética en una diferencia genética y, en consecuencia, analizable mediante diferentes técnicas. Sodium bisulfite (BS) treatment converts unmethylated cytosines into uracils, and thymine and therefore, the vast majority of the genome is reduced to three bases (A, G, and T) instead of four. Therefore, in order to analyze the methylation pattern, the design of specific assays for the DNA converted by BS is necessary. This treatment converts an epigenetic modification into a genetic difference and, consequently, can be analyzed using different techniques.
La conversión por bisulfito sódico se considera el "gold standard' para el análisis de la metilación del ADN, dada su potencial alta resolución cuando se combina con métodos de secuenciación. Conversion by sodium bisulfite is considered the 'gold standard' for the analysis of DNA methylation, given its high resolution potential when combined with sequencing methods.
Dentro de las técnicas de análisis locus-especifico se encuentran, entre otras, la metilación específica mediante PCR (MSP, del inglés Methyl Specific PCR), la secuenciación por bisulfito (BSP, del inglés Bisulfite Sequencing PCR) y la pirosecuenciación por bisulfito. Los autores de la invención han desarrollado una estrategia para la clasificación molecular del MB basada en el análisis de los patrones de metilación de un grupo de citosinas diferencialmente metiladas (Panel WNT-SHH y Panel G3-G4). El patrón de metilación de dichas citosinas puede ser analizado mediante diversas técnicas aplicables para el análisis de metilación del ADN. Los autores de la invención han corroborado la validez de los Paneles de citosinas utilizando datos de metilación del ADN generados mediantes diversas aproximaciones, entre éstas la tecnología de microarrays y técnicas moleculares como son las aproximaciones basadas en la conversión del ADN con bisulfito complementadas con la amplificación por una reacción en cadena de la ADN polimerasa (PCR, del inglésAmong the locus-specific analysis techniques are, among others, the specific methylation by PCR (MSP), bisulfite sequencing (BSP) and bisulfite pyrosequencing. The authors of the invention have developed a strategy for the molecular classification of MB based on the analysis of the methylation patterns of a group of differentially methylated cytosines (Panel WNT-SHH and Panel G3-G4). The methylation pattern of such cytosines can be analyzed by various applicable techniques for DNA methylation analysis. The authors of the invention have corroborated the validity of cytosine Panels using DNA methylation data generated through various approaches, including microarray technology and molecular techniques such as approaches based on the conversion of bisulfite DNA supplemented with amplification. by a DNA polymerase chain reaction (PCR)
Polymerase Chain Reaction) o métodos de secuenciacion (secuenciacion por bisulfito y Pirosecuenciacion por bisulfito). Polymerase Chain Reaction) or sequencing methods (bisulfite sequencing and bisulfite sequencing).
Asimismo, los autores de la invención han demostrado que la estrategia propuesta para la clasificación molecular del MB puede ser aplicada a ADN extraído a partir de todo tipo de muestras con una representación adecuada de ADN tumoral, como pueden ser biopsias de tejido tumoral obtenido en fresco (F) y congelado y conservado a -80°C (FF, del inglés fres/7 frozerí) o fijado en formalina tamponada 10% y embebido en parafina (FFPE, del inglés formalin fixed, paraffin-embedded). Likewise, the authors of the invention have demonstrated that the proposed strategy for the molecular classification of MB can be applied to DNA extracted from all types of samples with an adequate representation of tumor DNA, such as fresh tissue biopsy. (F) and frozen and preserved at -80 ° C (FF, from English fres / 7 frozerí) or fixed in 10% buffered formalin and embedded in paraffin (FFPE, from English formalin fixed, paraffin-embedded).
El valor de esta estrategia de clasificación viene dado tanto por el número reducido de citosinas que componen el Panel WNT-SHH y Panel G3-G4, como por la viabilidad a nivel técnico (pirosecuenciacion, MSP, BSP o todas aquellas técnicas que permitan determinar de forma directa o indirecta, el patrón de metilación de una secuencia de interés), la aplicabilidad a biopsias de tejido tumoral pequeñas obtenidas en F/FF, FFPE y/o biopsias líquidas, la elevada precisión, rapidez, fácil interpretación, reproducibilidad de los resultados, y por el bajo coste económico. The value of this classification strategy is given both by the reduced number of cytosines that make up Panel WNT-SHH and Panel G3-G4, as well as viability at the technical level (pyrosequencing, MSP, BSP or all those techniques that allow determining directly or indirectly, the pattern of methylation of a sequence of interest), the applicability to small tumor tissue biopsies obtained in F / FF, FFPE and / or liquid biopsies, high precision, speed, easy interpretation, reproducibility of the results , and for the low economic cost.
Descripción de las figuras Description of the figures
Figura 1.- Normalización, control de calidad y filtrado de los datos brutos de metilación generados mediante tecnología de microarray (micromatriz) de alta densidad ¡Ilumina Human Methylation BeadChip 450K. Cohorte de estudio, 106 meduloblastomas en F/FF. (A) Diagrama de densidades del conjunto de datos de metilación; (B) Representación gráfica del control de calidad de la conversión por bisulfito del ADN; (C) Diagrama de densidades de los datos normalizados mediante la metodología SWAN. Figure 1.- Standardization, quality control and filtering of raw methylation data generated by high density microarray (microarray) technology Illuminates Human Methylation BeadChip 450K. Study cohort, 106 medulloblastomas in F / FF. (A) Density diagram of the methylation data set; (B) Graphical representation of the quality control of bisulfite conversion of DNA; (C) Diagram of densities of normalized data using the SWAN methodology.
Valor β (β -valué): estimación del nivel de metilación mediante la proporción entre el valor del alelo metilado y el no-metilado (metilado / no-metilado + metilado +100). Β value (β -valué): estimation of the level of methylation by the ratio between the value of the methylated and non-methylated allele (methylated / non-methylated + methylated +100)
Figura 2.- Análisis no supervisado de los niveles de metilación de la cohorte de estudio, 106 meduloblastomas en F/FF. Se definen el conjunto de los perfiles de metilación de todas las muestras. (A) Análisis de la distribución de la variabilidad (density plof) de metilación de ADN de las muestras; (B) Análisis de Componentes Principales (ACP) y (C) análisis de clustering jerárquico de todas las CpGs con desviación estándar mayor o igual a 0,3 (5.904 CpGs).  Figure 2.- Unsupervised analysis of the methylation levels of the study cohort, 106 medulloblastomas in F / FF. The set of methylation profiles of all samples are defined. (A) Analysis of the distribution of the variability (density plof) of DNA methylation of the samples; (B) Principal Component Analysis (ACP) and (C) hierarchical clustering analysis of all CpGs with standard deviation greater than or equal to 0.3 (5,904 CpGs).
Figura 3.- Análisis no supervisado empleando el conjunto de las nueve citosinas que componen el Panel WNT-SHH. Cohorte de estudio, 106 muestras de meduloblastomas en F/FF. (A) ACP no supervisado con las 9 CpGs del Panel WNT-SHH (3 grupos); (B) Representación gráfica (gráfico de violín) del patrón de metilación diferencial de las citosinas del Panel WNT-SHH en los subgrupos WNT, SHH y no-WNT/no-SHH; (C) Comparación de los valores de metilación de las citosinas del Panel WNT-SHH en otros tejidos normales y tumorales. Figure 3.- Unsupervised analysis using the set of the nine cytosines that make up the WNT-SHH Panel. Study cohort, 106 samples of medulloblastomas in F / FF. (A) ACP not supervised with the 9 CpGs of the WNT-SHH Panel (3 groups); (B) Graphical representation (violin plot) of the differential methylation pattern of the WNT-SHH Panel cytosines in the WNT, SHH and non-WNT / non-SHH subgroups; (C) Comparison of the cytosine methylation values of the WNT-SHH Panel in other normal and tumor tissues.
Acrónimos. GS: glándula suprarrenal; ESC: células madre embrionarias (embryonal stem cells); IPSC: células madre pluripotentes inducidas (induced pluripotent stem cells); NPSC: células neuronales progenitoras; GPSC: células gliales progenitoras; GB: glioblastoma; DIPG: glioma difuso de protuberancia (Difused intrinsic pontine glioma); PA: astrocitoma pilocitico (pilocytic astrocitoma); Acronyms GS: adrenal gland; ESC: embryonic stem cells; IPSC: induced pluripotent stem cells; NPSC: progenitor neuronal cells; GPSC: progenitor glial cells; GB: glioblastoma; DIPG: diffuse intrinsic pontine glioma; PA: pyrocytic astrocytoma (pilocytic astrocytoma);
ATRT: tumor teradoide/rabdoide atípico (atypical teradoid/rhabdoid tumor); NB: neuroblastoma; GN: ganglioneuroma. ATRT: atypical teradoid / rhabdoid tumor (atypical teradoid / rhabdoid tumor); NB: neuroblastoma; GN: ganglioneuroma.
Figura 4. Análisis no supervisado empleando el conjunto de las ocho citosinas que componen el Panel G3-G4. Meduloblastomas Grupo 3 y Grupo 4 de la cohorte de estudio, 106 tumores en F/FF. (A) ACP no supervisado con las Figure 4. Unsupervised analysis using the set of eight cytosines that make up Panel G3-G4. Medulloblastomas Group 3 and Group 4 of the study cohort, 106 tumors in F / FF. (A) ACP not supervised with the
8 CpGs del Panel G3-G4 (2 grupos); (B) Representación gráfica (gráfico de violín) del patrón de metilación diferencial de las citosinas del Panel G3-G4 en los subgrupos Grupo 3 y Grupo 4; (C) Comparación de los valores de metilación de las citosinas del Panel G3-G4 en otros tejidos normales y tumorales. 8 CpGs of Panel G3-G4 (2 groups); (B) Graphical representation (violin plot) of the differential methylation pattern of the cytosines of Panel G3-G4 in the subgroups Group 3 and Group 4; (C) Comparison of methylation values of the cytosines of Panel G3-G4 in other normal and tumor tissues.
Acrónimos. GS: glándula suprarrenal; ESC: células madre embrionarias (embryonal stem cells); IPSC: células madre pluripotentes inducidas (induced pluripotent stem cells); NPSC: células neuronales progenitoras; GPSC: células gliales progenitoras; GB: glioblastoma; DIPG: glioma difuso de protuberanciaAcronyms GS: adrenal gland; ESC: embryonic stem cells; IPSC: induced pluripotent stem cells; NPSC: progenitor neuronal cells; GPSC: progenitor glial cells; GB: glioblastoma; DIPG: diffuse protrusion glioma
(Difused intrinsic pontine glioma); PA: astrocitoma pilocitico (pilocytic astrocitoma); ATRT: tumor teradoide/rabdoide atípico (atypical teradoid/rhabdoid tumor); NB: neuroblastoma; GN: ganglioneuroma. (Difused intrinsic pontine glioma); PA: pyrocytic astrocytoma (pilocytic astrocytoma); ATRT: atypical teradoid / rhabdoid tumor (atypical teradoid / rhabdoid tumor); NB: neuroblastoma; GN: ganglioneuroma.
Figura 5.- Validación del Panel WNT-SHH y Panel G3-G4 mediante el empleo de la base de datos HM450k de metilacion del ADN. Cohorte de validación, 169 muestras de meduloblastoma FFPE. (A) Análisis no supervisado mediante ACP empleando el conjunto de las nueve citosinas que componen el Panel WNT-SHH (9 CpGs); (B) ACP de los valores de metilacion de las citosinas identificadas en el Panel G3-G4 (8 CpGs) en muestras de meduloblastoma FFPE.  Figure 5.- Validation of Panel WNT-SHH and Panel G3-G4 by using the HM450k DNA methylation database. Validation cohort, 169 samples of FFPE medulloblastoma. (A) Analysis not supervised by ACP using the set of the nine cytosines that make up the WNT-SHH Panel (9 CpGs); (B) ACP of the cytosine methylation values identified in Panel G3-G4 (8 CpGs) in FFPE medulloblastoma samples.
Figura 6.- Análisis de patrón de metilacion del Panel WNT-SHH mediante metodología de secuenciacion por bisulfito (BSP) en ADN de tejido F/FF y FFPE de meduloblastoma. Los círculos muestran el patrón de metilacion diferencial de las nueve citosinas de interés del Panel WNT-SHH, (rojo) estado identificativo y (verde) estado excluyente del subgrupo. (A) Subgrupo WNT; (B) Subgrupo SHH y (C) Subgrupo no-WNT/no-SHH. Figure 6.- Analysis of the methylation pattern of the WNT-SHH Panel by bisulfite sequencing methodology (BSP) in F / FF tissue DNA and medulloblastoma FFPE. The circles show the differential methylation pattern of the nine cytosines of interest of the WNT-SHH Panel, (red) identifying status and (green) excluding status of the subgroup. (A) WNT subgroup; (B) SHH Subgroup and (C) Non-WNT / Non-SHH Subgroup.
Figura 7.- Ejemplo gráfico de los niveles de metilacion de las citosinas del Panel WNT-SHH obtenidos mediante pirosecuenciación por bisulfito en ADN de tejido F/FF y FFPE de meduloblastoma. Descripción de la invención  Figure 7.- Graphic example of the levels of methylation of the cytosines of the WNT-SHH Panel obtained by pyrosequencing by bisulfite in DNA of tissue F / FF and FFPE of medulloblastoma. Description of the invention
La presente invención tiene como principal objetivo identificar un marcador para la clasificación de pacientes con meduloblastoma (MB) que constituya una prueba más fácilmente aplicable que los sistemas de clasificación existentes, que sea reproducible y con una buena relación coste-efectividad en la práctica clínica. The present invention has as main objective to identify a marker for the classification of patients with medulloblastoma (MB) that constitutes a more easily applicable test than the existing classification systems, which is reproducible and with a good cost-effectiveness in clinical practice.
En la última revisión de la clasificación de la Organización Mundial de la Salud (OMS) de los tumores del sistema nervioso central publicado en 2016, se introdujeron las siguientes entidades definidas genéticamente: WNT, SHH y Grupo no-WNT/no-SHH. El Grupo 3 y Grupo 4 al tener un cierto grado de similitud y al coincidir algunas de las características genéticas, fueron incluidas de forma provisional en el subgrupo de MBs "no-WNT/no-SHH". In the latest revision of the classification of the World Health Organization (WHO) of tumors of the central nervous system published in 2016, They introduced the following genetically defined entities: WNT, SHH and Non-WNT / non-SHH Group. Group 3 and Group 4, having a certain degree of similarity and coinciding with some of the genetic characteristics, were provisionally included in the subgroup of "non-WNT / no-SHH" MBs.
En respuesta a la necesidad de aplicar en la práctica clínica este sistema de clasificación molecular de pacientes con MB para determinar el riesgo clínico y poder definir el tratamiento más adecuado para cada paciente, en la presente invención se definen dos paneles de citosinas que, en combinación, actúan como marcador de clasificación molecular eficaz de pacientes con MB en cuatro subgrupos moleculares: WNT, SHH, Grupo 3 y Grupo 4. In response to the need to apply this molecular classification system of patients with MB in clinical practice to determine the clinical risk and to define the most appropriate treatment for each patient, in the present invention two cytosine panels are defined which, in combination , act as an effective molecular classification marker for patients with MB in four molecular subgroups: WNT, SHH, Group 3 and Group 4.
Para ello, los autores de la presente invención han corroborado que existe en MB una asociación del patrón de metilación del ADN con las entidades genéticas WNT, SHH, y no-WNT/no-SHH. A partir de estos patrones de metilación, los autores han seleccionado un primer panel de nueve citosinas, con un patrón de metilación diferencial, que se asocia de forma significativa y precisa con cada uno de los subgrupos WNT, SHH, y no-WNT/no-SHH. Han demostrado que este panel de nueve citosinas (de aquí en adelante Panel WNT-SHH) (Tabla 1) es eficaz para establecer las tres entidades genéticas definidas por la OMS (2016): WNT, SHH y Grupo no-WNT/no-SHH. To this end, the authors of the present invention have confirmed that there is in MB an association of the DNA methylation pattern with the genetic entities WNT, SHH, and non-WNT / non-SHH. From these methylation patterns, the authors have selected a first panel of nine cytosines, with a differential methylation pattern, which is significantly and accurately associated with each of the WNT, SHH, and non-WNT / no subgroups. -SHH. They have shown that this panel of nine cytosines (hereinafter WNT-SHH Panel) (Table 1) is effective in establishing the three genetic entities defined by WHO (2016): WNT, SHH and Non-WNT / non-SHH Group .
Diversas combinaciones de dos o más de estas citosinas tienen la capacidad de clasificar correctamente los MBs en estos subgrupos, siendo dichas combinaciones susceptibles de representar potenciales marcadores adecuados para la clasificación de estos tumores. Various combinations of two or more of these cytosines have the ability to correctly classify MBs in these subgroups, such combinations being able to represent potential markers suitable for the classification of these tumors.
Tabla 1. Panel WNT-SHH. Table 1. WNT-SHH panel.
Posición Posición Position Position
Subgrupo ID citosina ID lllumina Cromosoma Subgroup ID cytosine ID lllumina Chromosome
inicio final final start
WNT WNT1_MB cg25542041 9 124982087 124982088WNT WNT1_MB cg25542041 9 124982087 124982088
WNT WNT2_MB cg24280645 17 48636900 48636901WNT WNT2_MB cg24280645 17 48636900 48636901
WNT WNT3_MB cg02227036 16 50425329 50425330 no-WNT WNT3_MB cg02227036 16 50425329 50425330 no-
WNT/no- N-WS1_MB cg 18849583 14 32836157 32836158 SHH WNT / no- N-WS1_MB cg 18849583 14 32836157 32836158 SHH
no- no-
WNT/no- N-WS2_MB cg 19828869 2 171552304 171552305 SHH WNT / no- N-WS2_MB cg 19828869 2 171552304 171552305 SHH
no- no-
WNT/no- N-WS3_MB cg01268345 7 138603645 138603646 SHHWNT / no- N-WS3_MB cg01268345 7 138603645 138603646 SHH
SHH SHH1_MB cg10333416 16 844474 844475SHH SHH1_MB cg10333416 16 844474 844475
SHH SHH2_MB cg 10959440 6 148701916 148701917SHH SHH2_MB cg 10959440 6 148701916 148701917
SHH SHH3_MB cg 12925355 2 234386471 234386472 SHH SHH3_MB cg 12925355 2 234386471 234386472
Tal y como se puede observar en la Tabla 1 , el panel WNT-SHH está formado por 9 citosinas denominadas como WNT1_MB, WNT2_MB, WNT3_MB, N-WS1_MB, N-WS2_MB, N-WS3_MB, SHH1_MB, SHH2_MB y SHH3_MB. As can be seen in Table 1, the WNT-SHH panel consists of 9 cytosines called WNT1_MB, WNT2_MB, WNT3_MB, N-WS1_MB, N-WS2_MB, N-WS3_MB, SHH1_MB, SHH2_MB and SHH3_MB.
Cada subgrupo molecular de MB (WNT, SHH y no-WNT/no-SHH) está asociado de forma específica y unívoca con un patrón de metilación diferencial de las citosinas del Panel WNT-SHH. Cada citosina muestra un patrón de metilación bimodal específico: niveles muy elevados de metilación (promedio valor metilación ≥ 80%) o al contrario, niveles muy bajos (promedio valor metilación≤ 17%) para cada uno de los subgrupos. Each MB molecular subgroup (WNT, SHH and non-WNT / non-SHH) is specifically and uniquely associated with a differential methylation pattern of the cytosines of the WNT-SHH Panel. Each cytosine shows a specific bimodal methylation pattern: very high levels of methylation (average methylation value ≥ 80%) or conversely, very low levels (average methylation value ≤ 17%) for each of the subgroups.
Aquellos tumores con un patrón de metilación con valores elevados en las citosinas WNT1_MB y WNT2_MB y niveles bajos de metilación en WNT3_MB, se asocian de forma específica y univoca con el subgrupo WNT de MBs. Cuando se observan valores de metilación elevados en las citosinas SHH 1_MB y SHH2_MB y bajos en SHH3_MB, este patrón define de forma univoca y directa el subgrupo SHH. Valores elevados en N-WS1_MB y N-WS2_MB, y bajos en N-WS3_MB son indicadores de un tumor que pertenece al subgrupo no-WNT/no-SHH de MB. En la tabla esquemática descrita a continuación (Tabla 2) pueden verse los patrones de metilación referencia para el panel WNT-SHH. Tabla 2. Patrón de metilación referencia de las citosinas que constituyen el Panel WNT-SHH para los subgrupos WNT, SHH y no-WNT/no-SHH. Those tumors with a methylation pattern with high values in the cytosines WNT1_MB and WNT2_MB and low levels of methylation in WNT3_MB, are specifically and univocally associated with the WNT subgroup of MBs. When high methylation values are observed in the SHH 1_MB and SHH2_MB cytosines and low in SHH3_MB, this pattern defines the SHH subgroup univocally and directly. High values in N-WS1_MB and N-WS2_MB, and low in N-WS3_MB are indicators of a tumor that belongs to the non-WNT / non-SHH subgroup of MB. The reference methylation patterns for the WNT-SHH panel can be seen in the schematic table described below (Table 2). Table 2. Reference methylation pattern of the cytosines that constitute the WNT-SHH Panel for the WNT, SHH and non-WNT / non-SHH subgroups.
Figure imgf000019_0001
Figure imgf000019_0002
Figure imgf000019_0003
Figure imgf000019_0001
Figure imgf000019_0002
Figure imgf000019_0003
El símbolo "+" representa niveles muy elevados de metilación (promedio valor metilación≥ 80%), mientras que el símbolo "-" representa niveles muy bajos de metilación (promedio valor metilación≤ 17%). The "+" symbol represents very high levels of methylation (average methylation value ≥ 80%), while the "-" symbol represents very low levels of methylation (average methylation value ≤ 17%).
Asimismo, los autores de la invención han identificado un segundo panel de 8 citosinas (de aquí en adelante, Panel G3-G4) como marcador para diferenciar de forma eficaz las dos entidades genéticas Grupo 3 y Grupo 4, actualmente incluidas de forma provisional en el subgrupo no-WNT/no-SHH de la OMS. Likewise, the authors of the invention have identified a second panel of 8 cytosines (hereinafter, Panel G3-G4) as a marker to effectively differentiate the two genetic entities Group 3 and Group 4, currently included provisionally in the WHO non-WNT / non-SHH subgroup.
Tabla 3. Panel G3-G4 Table 3. Panel G3-G4
ID Posición Posición ID Position Position
Subgrupo ID lllumina Cromosoma Subgroup ID lllumina Chromosome
Citosina Inicio Final  Cytosine Final Start
Grupo 3 Gr3-A_MB cg 13548946 12 123350077 123350078 Group 3 Gr3-A_MB cg 13548946 12 123350077 123350078
Grupo 3 Gr3-B_MB cg05679609 12 30671926 30671927Group 3 Gr3-B_MB cg05679609 12 30671926 30671927
Grupo 3 Gr3-C_MB cg09929238 17 78560916 78560917Group 3 Gr3-C_MB cg09929238 17 78560916 78560917
Grupo 3 Gr3-D_MB Cg24044478 8 145035191 145035192Group 3 Gr3-D_MB C g24044478 8 145035191 145035192
Grupo 4 Gr4-A_MB cg08129331 17 78560478 78560479Group 4 Gr4-A_MB cg08129331 17 78560478 78560479
Grupo 4 Gr4-B_MB cg 10400652 19 46996516 46996517 Grupo 4 Gr4-C_MB cg 12565585 8 105235943 105235944Group 4 Gr4-B_MB cg 10400652 19 46996516 46996517 Group 4 Gr4-C_MB cg 12565585 8 105235943 105235944
Grupo 4 Gr4-D_MB cg16167052 19 46996347 46996348 Group 4 Gr4-D_MB cg16167052 19 46996347 46996348
Diversas combinaciones de dos o más de estas citosinas tienen la capacidad de clasificar correctamente los MBs en estos subgrupos, siendo dichas combinaciones susceptibles de representar potenciales marcadores adecuados para la clasificación de estos tumores. Various combinations of two or more of these cytosines have the ability to correctly classify MBs in these subgroups, such combinations being able to represent potential markers suitable for the classification of these tumors.
Tal y como se puede observar en la Tabla 3, el Panel G3-G4 está formado por 8 citosinas denominadas como Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4- A_MB, Gr4-B_MB, Gr4-C_MB y Gr4-D_MB. As can be seen in Table 3, Panel G3-G4 consists of 8 cytosines called Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB.
Cada subgrupo molecular de MB está asociado de forma específica y unívoca con un patrón de metilación diferencial de las citosinas del Panel G3-G4. Cada citosina muestra un patrón de metilación bimodal específico: niveles muy elevados de metilación (promedio valor metilación ≥ 75%) o al contrario, niveles muy bajos (promedio valor metilación≤ 20%) para cada uno de los subgrupos. Each molecular subgroup of MB is specifically and uniquely associated with a differential methylation pattern of the cytosines of Panel G3-G4. Each cytosine shows a specific bimodal methylation pattern: very high levels of methylation (average methylation value ≥ 75%) or conversely, very low levels (average methylation value ≤ 20%) for each of the subgroups.
Aquellos tumores con un patrón de metilación con valores elevados (≥ 75%) en las citosinas Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB y Gr4-D_MB, se asocian de forma específica y unívoca con el subgrupo Grupo 3 de MBs. Mientras que valores bajos en las citosinas Gr3-A_MB, Gr3- B_MB, Gr3-C_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB y Gr4-D_MB son indicadores de un tumor que pertenece al subgrupo Grupo 4. En la tabla esquemática descrita a continuación (Tabla 4) pueden verse los patrones de metilación referencia para el panel G3-G4. Those tumors with a methylation pattern with high values (≥ 75%) in the cytosines Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB, they are specifically and uniquely associated with the subgroup Group 3 of MBs. While low values in cytosines Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB are indicators of a tumor belonging to subgroup Group 4. The reference methylation patterns for panel G3-G4 can be seen in the schematic table described below (Table 4).
Tabla 4. Patrón de metilación de referencia de las citosinas que constituyen el Panel G3-G4 para los subgrupos Grupo 3 y Grupo 4. Table 4. Reference methylation pattern of the cytosines that constitute Panel G3-G4 for the subgroups Group 3 and Group 4.
ID Citosina Grupo 3 ID Citosina Grupo 4  Cytosine ID Group 3 Cytosine ID Group 4
Gr3-A_MB + Gr3-A_MB - Gr3-A_MB + Gr3-A_MB -
Gr3-B_MB + Gr3-B_MB -Gr3-B_MB + Gr3-B_MB -
Gr3-C_MB + Gr3-C_MB - Gr3-D_MB + Gr3-D_MB -Gr3-C_MB + Gr3-C_MB - Gr3-D_MB + Gr3-D_MB -
Gr4-A_MB + Gr4-A_MB -Gr4-A_MB + Gr4-A_MB -
Gr4-B_MB + Gr4-B_MB -Gr4-B_MB + Gr4-B_MB -
Gr4-C_MB + Gr4-C_MB -Gr4-C_MB + Gr4-C_MB -
Gr4-D_MB + Gr4-D_MB - Gr4-D_MB + Gr4-D_MB -
El símbolo "+" representa niveles muy elevados de metilación (promedio valor metilación≥ 75%), mientras que el símbolo "-" representa niveles muy bajos de metilación (promedio valor metilación≤ 20%). The "+" symbol represents very high levels of methylation (average methylation value ≥ 75%), while the "-" symbol represents very low levels of methylation (average methylation value ≤ 20%).
En base a estos desarrollos, en un aspecto principal de la invención se contempla el perfil de metilación de las citosinas del panel WNT-SHH (y sus diferentes combinaciones) para su empleo como marcador para la clasificación de pacientes con MB en los tres subgrupos moleculares definidos por la OMS (2016): WNT, SHH y Grupo no-WNT/no-SHH. Adicionalmente, para aquellos MBs clasificados como no-WNT/no-SHH con el Panel WNT-SHH, se contempla el perfil de metilación de las citosinas del Panel G3-G4 (y sus diferentes combinaciones) para su empleo como marcador para la clasificación de pacientes con MB en los subgrupos moleculares Grupo 3 y Grupo 4.  Based on these developments, in a main aspect of the invention the methylation profile of the cytosines of the WNT-SHH panel (and its different combinations) is contemplated for use as a marker for the classification of patients with MB in the three molecular subgroups defined by WHO (2016): WNT, SHH and Non-WNT / non-SHH Group. Additionally, for those MBs classified as non-WNT / non-SHH with the WNT-SHH Panel, the cytosine methylation profile of Panel G3-G4 (and its different combinations) is contemplated for use as a marker for the classification of MB patients in the molecular subgroups Group 3 and Group 4.
El análisis del patrón de metilación de las citosinas propuestas permite contrastar los niveles de metilación diferencial entre subgrupos de MB con comportamiento clínico distinto, lo que permite clasificar los tumores según su evolución clínica, y establecer el tratamiento más adecuado para cada paciente. The analysis of the methylation pattern of the proposed cytosines allows to contrast the levels of differential methylation between MB subgroups with different clinical behavior, which allows classifying the tumors according to their clinical evolution, and establishing the most appropriate treatment for each patient.
Los autores de la invención han demostrado que el análisis de dichos marcadores (Panel WNT-SHH y Panel G3-G4) es fácilmente aplicable en la práctica clínica y mejora la relación coste-efectividad de los métodos propuestos hasta la fecha. The authors of the invention have shown that the analysis of said markers (Panel WNT-SHH and Panel G3-G4) is easily applicable in clinical practice and improves the cost-effectiveness of the methods proposed to date.
Así, en otro aspecto principal de la invención se contempla un método in vitro para la clasificación de un paciente con meduloblastoma en uno de los subgrupos moleculares WNT, SHH y grupo no-WNT/no-SHH que comprende los siguientes pasos: a) Análisis de los niveles de metilacion de las citosinas WNT1_MB, WNT2_MB, WNT3_MB, N-WS1_MB, N-WS2_MB, N-WS3_MB, SHH1_MB, SHH2_MB y SHH3_MB, que forman el panel WNT-SHH, o una combinación de las mismas, en el ADN extraído de una muestra biológica aislada del paciente, y b) Clasificación del paciente en uno de los subgrupos moleculares WNT, SHH y grupo no-WNT/no-SHH en base a los niveles de metilacion de las citosinas analizadas del panel WNT-SHH, según los valores de referencia de la Tabla 2. Thus, in another main aspect of the invention an in vitro method is contemplated for the classification of a patient with medulloblastoma in one of the molecular subgroups WNT, SHH and non-WNT / non-SHH group comprising the following steps: a) Analysis of the levels of methylation of the cytosines WNT1_MB, WNT2_MB, WNT3_MB, N-WS1_MB, N-WS2_MB, N-WS3_MB, SHH1_MB, SHH2_MB and SHH3_MB, which form the WNT-SHH panel, or a combination thereof, in the DNA extracted from a biological sample isolated from the patient, and b) Classification of the patient in one of the WNT, SHH and non-WNT / non-SHH molecular subgroups based on the levels of cytosine methylation analyzed in the WNT- panel SHH, according to the reference values in Table 2.
En una realización particular, para aquellos pacientes clasificados en el paso b) del método de la invención como no-WNT/no-SHH, se llevan a cabo los siguientes pasos adicionales: c) Análisis de los niveles de metilacion de las citosinas Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB y Gr4-D_MB, o una combinación de las mismas, que forman el panel G3-G4, en el ADN extraído de la muestra biológica aislada del paciente, y In a particular embodiment, for those patients classified in step b) of the method of the invention as non-WNT / non-SHH, the following additional steps are carried out: c) Analysis of the levels of methylation of cytosines Gr3- A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB, or a combination thereof, which form panel G3-G4, in the DNA extracted from the biological sample isolated from the patient, and
d) Clasificación del paciente en uno de los subgrupos moleculares Grupo 3 y Grupo 4 en base a los niveles de metilacion de las citosinas analizadas del panel G3-G4, según los valores de referencia de la Tabla 4. d) Classification of the patient in one of the molecular subgroups Group 3 and Group 4 based on the levels of methylation of the cytosines analyzed in panel G3-G4, according to the reference values in Table 4.
En otro aspecto principal de la invención se contempla el método para la clasificación de un paciente con meduloblastoma en uno de los subgrupos moleculares WNT, SHH, Grupo 3 y Grupo 4, que comprende: Another method of the invention contemplates the method for classifying a patient with medulloblastoma into one of the molecular subgroups WNT, SHH, Group 3 and Group 4, which comprises:
A. Análisis de forma combinada de los niveles de metilacion de las citosinas del panel WNT-SHH, o una combinación de las mismas, y del panel G3-G4, o una combinación de las mismas, en el ADN extraído de una muestra biológica aislada del paciente, y A. Combined analysis of the levels of methylation of the cytosines of the WNT-SHH panel, or a combination thereof, and of the G3-G4 panel, or a combination thereof, in the DNA extracted from an isolated biological sample of the patient, and
B. Clasificación del paciente en uno de los subgrupos moleculares WNT, SHH, Grupo 3 y Grupo 4, en base a los niveles de metilacion las citosinas analizadas del panel WNT-SHH y del panel G3-G4, según los valores de referencia de las Tablas 2 y 4. A efectos de la presente invención, la expresión "una combinación de las mismas" se refiere a cualquier combinación de dos o más citosinas de aquellas que forman los paneles WNT-SHH o G3-G4. Para la realización del método de clasificación de la invención se parte de una muestra biológica aislada de un paciente y se procede con la extracción de ADN, mediante protocolos convencionales, tratamiento del ADN y posterior análisis de los niveles de metilación de cada una de las citosina de interés. Los datos obtenidos se comparan con el panel de metilación referencia de citosinas para así establecer el subgrupo molecular al cual pertenece el tumor. B. Classification of the patient in one of the molecular subgroups WNT, SHH, Group 3 and Group 4, based on the levels of methylation the cytosines analyzed from the WNT-SHH panel and the G3-G4 panel, according to the reference values of the Tables 2 and 4. For the purposes of the present invention, the expression "a combination thereof" refers to any combination of two or more cytosines of those forming the WNT-SHH or G3-G4 panels. For the realization of the method of classification of the invention, a biological sample is isolated from a patient and the DNA is extracted by conventional protocols, DNA treatment and subsequent analysis of the levels of methylation of each cytosine of interest. The data obtained are compared with the cytosine reference methylation panel to establish the molecular subgroup to which the tumor belongs.
El método de clasificación de la invención puede ser realizado mediante diversas metodologías moleculares y aplicables a diversos tipos de tejido. De esta forma, el método de la invención permite su aplicación en la práctica clínica de la mayoría de los centros hospitalarios que tratan tumores pediátricos del sistema nervioso. The method of classification of the invention can be performed by various molecular methodologies and applicable to various types of tissue. In this way, the method of the invention allows its application in the clinical practice of most hospital centers that treat pediatric tumors of the nervous system.
En una realización preferida del método de clasificación de la invención, la muestra biológica empleada es tejido tumoral. En este caso, el método de clasificación molecular comprende obtener una muestra de tejido tumoral de meduloblastoma para el análisis del patrón de metilación de las citosinas del PanelIn a preferred embodiment of the method of classification of the invention, the biological sample used is tumor tissue. In this case, the molecular classification method comprises obtaining a sample of medulloblastoma tumor tissue for the analysis of the cytosine methylation pattern of the Panel.
WNT-SHH, del Panel G3-G4 o de la combinación de citosinas de ambos paneles. La muestra tumoral del paciente representa una porción de la pieza de tumor obtenida mediante cirugía o una biopsia del tejido tumoral. De forma preferida, la muestra tumoral utilizada para la realización del método de la invención tiene un contenido de célula tumoral viable superior al 70% (determinado por un anatomopatólogo). WNT-SHH, of Panel G3-G4 or of the combination of cytosines of both panels. The patient's tumor sample represents a portion of the tumor piece obtained by surgery or a biopsy of the tumor tissue. Preferably, the tumor sample used for carrying out the method of the invention has a viable tumor cell content greater than 70% (determined by a pathologist).
Dicha muestra puede ser obtenida bien a partir de biopsia tumoral en fresco sin fijar (F), o de biopsia tumoral congelada (FF, del inglés fresh frozen) conservada a -80°C o bien fijada en formalina tamponada al 10% y embebida en parafina (FFPE, del inglés formalin fixed paraffin embedded). This sample can be obtained either from fresh tumor biopsy without fixing (F), or from frozen tumor biopsy (FF) stored at -80 ° C or fixed in 10% buffered formalin and embedded in Paraffin (FFPE).
En una realización particular del método de clasificación de la invención, la muestra biológica es tejido tumoral en fresco (F). En este caso, el método de clasificación de MB comprende utilizar una muestra de tejido tumoral en fresco de meduloblastoma para el análisis del patrón de metilacion de la combinación de citosinas del Panel WNT-SHH y Panel G3-G4. En otra realización particular del método de clasificación de la invención la muestra biológica es tejido tumoral congelado (FF) y almacenado a -80°C hasta su uso. En este caso, el método de clasificación de MB comprende utilizar una muestra de tejido tumoral almacenado congelado de MB para el análisis del patrón de metilacion de las diferentes combinaciones de citosinas del Panel WNT-SHH y Panel G3-G4. In a particular embodiment of the method of classification of the invention, the biological sample is fresh tumor tissue (F). In this case, the method of MB classification comprises using a fresh tumor tissue sample of medulloblastoma for the analysis of the methylation pattern of the cytosine combination of Panel WNT-SHH and Panel G3-G4. In another particular embodiment of the method of classification of the invention the biological sample is frozen tumor tissue (FF) and stored at -80 ° C until use. In this case, the MB classification method comprises using a sample of frozen stored tumor tissue of MB for the analysis of the methylation pattern of the different combinations of cytosines of Panel WNT-SHH and Panel G3-G4.
En otra realización particular del método de clasificación de la invención la muestra biológica es tejido tumoral fijado en formalina tamponada al 10% y embebido en parafina (FFPE), por lo que pueden evaluarse muestras obtenidas en un laboratorio de anatomía-patológica estándar. In another particular embodiment of the method of classification of the invention the biological sample is tumor tissue fixed in 10% buffered formalin and embedded in paraffin (FFPE), whereby samples obtained in a standard pathology laboratory can be evaluated.
A los fines de la invención, la cuantificación/análisis de los niveles de metilacion de las citosinas que constituyen la invención se puede llevar a cabo mediante técnicas que permitan determinar de forma directa o indirecta el estado de metilacion de una secuencia de interés. For the purposes of the invention, the quantification / analysis of the levels of methylation of the cytosines that constitute the invention can be carried out by means of techniques that allow determining directly or indirectly the methylation status of a sequence of interest.
Así, el patrón de metilacion de las citosinas del Panel WNT-SHH y/o Panel G3-G4 puede ser analizado mediante diversas técnicas aplicables al análisis de metilacion del ADN como, por ejemplo, la tecnología de microarrays y técnicas moleculares basadas en la conversión del ADN con bisulfito de sodio, complementadas con la amplificación por una reacción en cadena de la ADN polimerasa (PCR, del inglés Polymerase Chain Reaction) y métodos de secuenciación (secuenciación por bisulfito y pirosecuenciación por bisulfito). La conversión del ADN con bisulfito de sodio (NaHSC ) es el paso inicial de varias técnicas, la mayoría de las cuales son complementadas con la amplificación por una reacción en cadena de la ADN polimerasa (PCR). La PCR es una técnica de amplificación selectiva in vitro de un fragmento concreto de ADN. El método se basa en una fase de desnaturalización de la doble hélice de ADN y la unión específica de dos oligonucleótidos (primers) que flanquean la región a amplificar y sirven de cebadores para iniciar la síntesis del fragmento. La extensión de la cadena a partir de los cebadores se obtiene por acción de una polimerasa específica que soporta altas temperaturas sin desnaturalizarse. Este proceso en 3 pasos se repite durante 25-40 ciclos en un aparato específico (termociclador) de forma que se consigue una amplificación exponencial del fragmento de interés. Thus, the cytosine methylation pattern of Panel WNT-SHH and / or Panel G3-G4 can be analyzed by various techniques applicable to DNA methylation analysis, such as microarray technology and molecular techniques based on conversion DNA with sodium bisulfite, supplemented by amplification by a DNA polymerase chain reaction (PCR) and sequencing methods (bisulfite sequencing and bisulfite pyrosequencing). Conversion of DNA with sodium bisulfite (NaHSC) is the initial step of several techniques, most of which are complemented by amplification by a DNA polymerase chain reaction (PCR). PCR is a technique of selective in vitro amplification of a specific DNA fragment. The method is based on a phase of denaturation of the DNA double helix and binding specific of two oligonucleotides (primers) that flank the region to be amplified and serve as primers to initiate fragment synthesis. The extension of the chain from the primers is obtained by the action of a specific polymerase that supports high temperatures without denaturing. This 3-step process is repeated for 25-40 cycles in a specific device (thermocycler) so that an exponential amplification of the fragment of interest is achieved.
El bisulfito induce la desaminación de las citosinas no metiladas las cuales se convierten en uracilos, mientras que las 5-metil citosinas no se ven afectadas y permanecen como citosinas. A continuación se procede con la amplificación conBisulfite induces deamination of unmethylated cytosines which become uracils, while 5-methyl cytosines are unaffected and remain cytosines. Then proceed with the amplification with
PCR de los fragmentos génicos de interés mediante el empleo de cebadores específicos para alelos metilados y alelos no metilados. A partir de aquí, cualquier método para detectar un cambio de nucleótido (secuenciación) puede ser utilizado para identificar la metilación en la secuencia de interés. PCR of the gene fragments of interest through the use of specific primers for methylated and non-methylated alleles. From here, any method to detect a nucleotide change (sequencing) can be used to identify methylation in the sequence of interest.
La secuenciación específica por bisulfito (BSP) permite realizar un mapeo de metilaciones alelo-específicas en citosinas de interés, añadiendo la posibilidad de observar las metilaciones, además de la secuencia nucleotídica. Para el análisis de citosinas metiladas, se compara la secuencia tratada con bisulfito con la secuencia control que no ha sido sometida a la acción del bisulfito. Aquellas citosinas que estuviesen metiladas aparecerán tras la PCR y la secuenciación como citosinas, mientras que en la muestra donde el bisulfito las ha trasformado en uracilo, serán observadas como una timina (Darst RP et al. Bisulfite Sequencing of DNA. Current Protocols of Molecular Biology (2010) doi:10. 1002/0471142727. mb0709s91). La secuenciación por bisulfito es una variante de la secuenciación automatizada, según el método Sanger. Bisulfite-specific sequencing (BSP) allows mapping of allele-specific methylations in cytosines of interest, adding the possibility of observing methylations, in addition to the nucleotide sequence. For the analysis of methylated cytosines, the bisulfite treated sequence is compared with the control sequence that has not been subjected to the action of bisulfite. Those cytosines that were methylated will appear after PCR and sequencing as cytosines, while in the sample where bisulfite has transformed them into uracil, they will be observed as a thymine (Darst RP et al. Bisulfite Sequencing of DNA. Current Protocols of Molecular Biology (2010) doi: 10. 1002/0471142727. Mb0709s91). Bisulfite sequencing is a variant of automated sequencing, according to the Sanger method.
La secuenciación de ácidos nucleicos según el método Sanger es una metodología empleada para determinar el orden de los nucleótidos en un fragmento de ADN. El principio del método Sanger es la utilización de dideoxinucleótidos trifosfatos (ddNTPs) (Sanger F, Nicklen S and Coulson AR. (1977) DNA sequencing with chain-terminating inhibitor. Proc Nati Acad Sci USA, 74(12): 5463-5467). Estos carecen del grupo hidroxilo del carbono 3' y su uso en una reacción de elongación de ADN implica que al incorporarse a la cadena esta no puede continuar la elongación, produciéndose varios fragmentos de ADN truncados de longitud variable. La identidad del nucleótido que termina la cadena en cada posición puede identificarse realizando cuatro reacciones por separado utilizando en cada una de ellas un ddNTP distinto (ddATP, ddCTP, ddTTP o ddGTP) (Franga LT et al. A review of DNA sequencing techniques. Q Rev Biophys 2002; 35(2): 169-200). Actualmente se utilizan ddNTPs marcados con fluorescencia, cada uno de ellos con un fluoroforo distinto, lo que permite realizar una única reacción de secuencia que incluye todos los ddNTPs (Franga LT et al). Así mismo, se ha automatizado el proceso. Para determinar la secuencia de ADN se carga la mezcla de síntesis en una máquina de secuenciación automatizada basada en electroforesis capilar. Estas máquinas utilizan un sistema capilar para la separación rápida de los fragmentos y un detector óptico que registra la emisión fluorescente, dando como resultado un cromatograma o electroferograma (gráfico de picos de colores, T rojo, G negro, C azul y A verde), de manera que viendo la sucesión de picos se puede leer (existen programas informáticos específicos) la secuencia que ha pasado por el capilar. De esta manera permite detectar la presencia de modificaciones respecto a una secuencia referencia normal. Así, en realizaciones particulares de la invención, el análisis de los niveles de metilación de las citosinas de interés, en ADN previamente tratado con bisulfito, se lleva a cabo por secuenciación específica de ADN tratado con bisulfito (BSP). La pirosecuenciación es un método de secuenciación del ADN que permite cuantificar en tiempo real la liberación de los pirofosfatos (PPi) que tiene lugar en el momento en que los nucleótidos son incorporados en la reacción de síntesis delNucleic acid sequencing according to the Sanger method is a methodology used to determine the order of nucleotides in a DNA fragment. The principle of the Sanger method is the use of dideoxynucleotide triphosphates (ddNTPs) (Sanger F, Nicklen S and Coulson AR. (1977) DNA sequencing with chain-terminating inhibitor. Proc Nati Acad Sci USA, 74 (12): 5463-5467) . These lack the 3 'carbon hydroxyl group and its use in a DNA elongation reaction implies that when it is incorporated into the chain it cannot continue elongation, producing several truncated DNA fragments of variable length. The identity of the nucleotide that terminates the chain in each position can be identified by performing four separate reactions using in each of them a different ddNTP (ddATP, ddCTP, ddTTP or ddGTP) (Franga LT et al. A review of DNA sequencing techniques. Q Rev Biophys 2002; 35 (2): 169-200). Currently fluorescently labeled ddNTPs are used, each with a different fluorophore, which allows for a single sequence reaction that includes all ddNTPs (Franga LT et al). Likewise, the process has been automated. To determine the DNA sequence, the synthesis mixture is loaded into an automated sequencing machine based on capillary electrophoresis. These machines use a capillary system for rapid separation of the fragments and an optical detector that records the fluorescent emission, resulting in a chromatogram or electropherogram (graph of colored peaks, red T, black G, blue C and green A), so that seeing the sequence of peaks you can read (there are specific computer programs) the sequence that has passed through the capillary. In this way it allows to detect the presence of modifications with respect to a normal reference sequence. Thus, in particular embodiments of the invention, the analysis of the levels of methylation of the cytosines of interest, in DNA previously treated with bisulfite, is carried out by specific sequencing of bisulfite treated DNA (BSP). Pyrosequencing is a method of DNA sequencing that allows quantifying in real time the release of pyrophosphates (PPi) that takes place at the moment when nucleotides are incorporated into the synthesis reaction of the
ADN. Se parte, como en el método Sanger, de una secuencia de interés y unos cebadores específicos, con enzimas y substrato, y nucleótidos sin marcar. La ADN polimerasa une un dNTP liberando PPi en el proceso. La enzima ATP-sulfurilasa convierte el PPi en ATP con ayuda del adenosin-fosfosulfato (APS). La luciferasa convierte el ATP en luz, con ayuda de la luciferina. El resultado final es, como con el método Sanger, picos de intensidad que permiten leer la secuencia del ADN. El análisis de los patrones de metilación del ADN mediante pirosecuenciación combina la sencillez del protocolo con la reproducibilidad, especificidad y la precisión del análisis, comparable con metodologías de alta resolución. La pirosecuenciación de ADN tratado con bisulfito permite un análisis cuantitativo y preciso de la metilación basándose en la secuenciación por síntesis. DNA It starts, as in the Sanger method, of a sequence of interest and specific primers, with enzymes and substrate, and unlabeled nucleotides. DNA polymerase binds a dNTP releasing PPi in the process. The enzyme ATP-sulfurylase converts PPi into ATP with the help of adenosine phosphosulfate (APS). Luciferase converts ATP into light, with the help of luciferin. The end result is, as with the Sanger method, intensity peaks that allow you to read the DNA sequence. The analysis of DNA methylation patterns by pyrosequencing combines the simplicity of the protocol with reproducibility, specificity and analysis accuracy, comparable with high resolution methodologies. Pyrosequencing of bisulfite treated DNA allows a quantitative and precise analysis of methylation based on sequencing by synthesis.
Así, en otra realización particular del método de clasificación de la invención, el análisis de los niveles de metilación de las combinaciones de citosinas que constituyen el Panel WNT-SHH y/o Panel G3-G4 se lleva a cabo mediante la metodología de pirosecuenciación del ADN convertido por bisulfito. Thus, in another particular embodiment of the method of classification of the invention, the analysis of the levels of methylation of the cytosine combinations that constitute Panel WNT-SHH and / or Panel G3-G4 is carried out by the pyrosequencing methodology of the DNA converted by bisulfite.
El valor de esta estrategia de clasificación molecular viene dado tanto por el número reducido de citosinas que componen el Panel WNT-SHH y Panel G3-G4, como por la viabilidad a nivel técnico (pirosecuenciación, BSP u otras técnicas que permitan determinar de forma directa o indirecta el estado de metilación del ADN), la aplicabilidad a biopsias de tejido tumoral pequeñas obtenidas F y/o FF (F/FF) y/o FFPE, la elevada precisión, rapidez, fácil interpretación y reproducibilidad de los resultados, y por el bajo coste económico. The value of this molecular classification strategy is given both by the reduced number of cytosines that make up Panel WNT-SHH and Panel G3-G4, as well as viability at the technical level (pyrosequencing, BSP or other techniques that allow direct determination or indirectly the state of DNA methylation), the applicability to small tumor tissue biopsies obtained F and / or FF (F / FF) and / or FFPE, the high precision, speed, easy interpretation and reproducibility of the results, and by The low economic cost.
En otro aspecto principal de la invención se contempla un kit para llevar a cabo el método in vitro para la clasificación de un paciente con meduloblastoma en uno de los subgrupos moleculares WNT, SHH y grupo no-WNT/no-SHH que comprende: In another main aspect of the invention a kit is contemplated for carrying out the in vitro method for the classification of a patient with medulloblastoma in one of the molecular subgroups WNT, SHH and non-WNT / non-SHH group comprising:
Un set de oligonucleótidos adecuado para el análisis de los niveles de metilación de las citosinas del panel WNT-SHH; y A set of oligonucleotides suitable for the analysis of the cytosine methylation levels of the WNT-SHH panel; Y
Todos los reactivos adecuados para la metodología empleada en el análisis de la metilación de dichas citosinas.  All reagents suitable for the methodology used in the analysis of the methylation of said cytosines.
El set de oligonucleótidos empleado para analizar el estado de metilación de las citosinas que constituyen el Panel WNT-SHH, mediante metodología BSP, son cebadores para secuenciación específicos para las citosinas de interés (citosinas problema y citosinas para el control de la eficiencia de la reacción de conversión del ADN con bisulfito sódico). En realizaciones preferidas, los oligonucleótidos empleados se seleccionan de aquellos que presentan las secuencias mostradas en SEQ ID No 1-18, específicas para las citosinas problema, y SEQ I D No 48, 49, 51 , 52, 54, 55, 57, 58, 60, 61 , 63, 64, 66, 67, 69 y 70, específicas para las citosinas control, y sus combinaciones. The oligonucleotide set used to analyze the methylation status of the cytosines that constitute the WNT-SHH Panel, using BSP methodology, are specific sequencing primers for the cytosines of interest (problem cytosines and cytosines for the control of reaction efficiency of DNA conversion with sodium bisulfite). In preferred embodiments, the oligonucleotides employed are selected from those that have the sequences shown in SEQ ID No 1-18, specific for the cytosine problem, and SEQ ID No 48, 49, 51, 52, 54, 55, 57, 58, 60, 61, 63, 64, 66, 67, 69 and 70, specific to control cytosines, and their combinations.
Los oligonucleótidos empleados para analizar el estado de metilación de las citosinas que constituyen el Panel WNT-SHH mediante metodología de pirosecuenciación, son cebadores y/o sondas de hibridación biotiniladas específicas para las citosinas de interés (citosinas problema y citosinas control de la conversión del ADN con bisulfito). En realizaciones preferidas, los oligonucleótidos empleados se seleccionan de aquellos que presentan las secuencias mostradas en SEQ ID 1-6, 9-14, 17, 18 y 35-47, específicas para las citosinas problema, y SEQ ID NO 48-71 , específicas para las citosinas control, y sus combinaciones. The oligonucleotides used to analyze the methylation status of the cytosines that constitute the WNT-SHH Panel by pyrosequencing methodology, are biotinylated hybridization primers and / or probes for the cytosines of interest (problem cytosines and control cytosines of DNA conversion with bisulfite). In preferred embodiments, the oligonucleotides employed are selected from those that have the sequences shown in SEQ ID 1-6, 9-14, 17, 18 and 35-47, specific for the problem cytosines, and SEQ ID NO 48-71, specific for control cytosines, and their combinations.
En otro aspecto principal de la invención se contempla un kit para llevar a cabo el método in vitro para la clasificación de un paciente con meduloblastoma en uno de los subgrupos moleculares WNT, SHH, grupo 3 y grupo 4 que comprende: In another main aspect of the invention a kit is contemplated for carrying out the in vitro method for the classification of a patient with medulloblastoma in one of the molecular subgroups WNT, SHH, group 3 and group 4 comprising:
Un set de oligonucleótidos adecuado para el análisis de los niveles de metilación de las citosinas del Panel WNT-SHH y G3-G4, en combinación; y A set of oligonucleotides suitable for the analysis of the levels of methylation of the cytosines of Panel WNT-SHH and G3-G4, in combination; Y
Reactivos adecuados para la metodología empleada en el análisis de la metilación de dichas citosinas.  Reagents suitable for the methodology used in the analysis of the methylation of these cytosines.
En realizaciones particulares, el set de oligonucléotidos empleado para analizar el estado de metilación de las citosinas que constituyen el Panel WNT-SHH y el Panel G3-G4 mediante metodología BSP, son cebadores para secuenciación, específicos para las citosinas de interés (citosinas problema y citosinas para el control de la eficiencia de la reacción de conversión del ADN con bisulfito sódico). En realizaciones preferidas, los oligonucleótidos empleados se seleccionan de aquellos que presentan secuencias mostradas en SEQ ID NO 1-34, siendo los oligonucleótidos de secuencias SEQ ID NO 1-18 específicos para las citosinas problema del panel WNT-SHH, y los oligonucleótidos de secuencias SEQ ID NO 19-34 específicos para las citosinas problema del panel G3-G4, y oligonucleótidos de secuencias SEQ ID No 48, 49, 51 , 52, 54, 55, 57, 58, 60, 61 , 63, 64, 66, 67, 69 y 70, específicos para las citosinas control, y sus combinaciones. En otra realización particular del método de clasificación de la invención, el set de oligonucleótidos empleado para analizar el estado de metilación de las citosinas que constituyen el Panel WNT-SHH y el Panel G3-G4 mediante metodología de pirosecuenciación, son cebadores y/o sondas de hibridación biotinilados específicos para las citosinas de interés (citosinas problema y citosinas control de la conversión del ADN con bisulfito). En realizaciones preferidas, los oligonucleótidos empleados se seleccionan de aquellos que presentan secuencias mostradas en SEQ ID NO 1-6, 9-14, 17-47 y 72-79, siendo los oligonucleótidos de secuencias SEQ ID NO 1-6, 9-14, 17, 18, 35-47, específicos para las citosinas problema del panel WNT-SHH y los oligonucleótidos de secuencias SEQ ID NOIn particular embodiments, the oligonucleotide set used to analyze the methylation status of the cytosines that constitute the WNT-SHH Panel and the G3-G4 Panel using BSP methodology, are primers for sequencing, specific for the cytosines of interest (problem cytosines and cytosines for the control of the efficiency of the reaction of conversion of DNA with sodium bisulfite). In preferred embodiments, the oligonucleotides used are selected from those that have sequences shown in SEQ ID NO 1-34, the oligonucleotides of SEQ ID NO 1-18 sequences specific for the problem cytosines of the WNT-SHH panel, and the sequence oligonucleotides. SEQ ID NO 19-34 specific to the G3-G4 panel problem cytosines, and sequence oligonucleotides SEQ ID No 48, 49, 51, 52, 54, 55, 57, 58, 60, 61, 63, 64, 66, 67, 69 and 70, specific for control cytosines, and combinations thereof. In another particular embodiment of the method of classification of the invention, the set of oligonucleotides used to analyze the state of methylation of the cytosines that constitute Panel WNT-SHH and Panel G3-G4 by pyrosequencing methodology, are primers and / or probes of biotinylated hybridization specific for the cytosines of interest (problem cytosines and control cytosines of DNA conversion with bisulfite). In preferred embodiments, the oligonucleotides employed are selected from those having sequences shown in SEQ ID NO 1-6, 9-14, 17-47 and 72-79, the oligonucleotides of sequences SEQ ID NO 1-6, 9-14 being , 17, 18, 35-47, specific for the problem cytosines of the WNT-SHH panel and the sequence oligonucleotides SEQ ID NO
19-34 y 72-79, específicos para las citosinas problema del panel G3-G4, y los oligonucleótidos de secuencias SEQ ID NO 48-71 , específicos para las citosinas control, y combinaciones de los mismos. En una realización preferida, el kit de la presente invención incluye: oligonucleótidos de tipo específicos para la metodología empleada, para la combinación de citosinas del Panel WNT-SHH y/o Panel G3-G4, oligonucleótidos de tipo específicos para las citosinas referencia control positivo/negativo, una mezcla maestra (master mix) que contiene una Taq polimerasa termoestable, un tampón adecuado y MgC a concentraciones óptimas, además de los dNTPs optimizados para la metodología. 19-34 and 72-79, specific for the cytosine problem of the G3-G4 panel, and the oligonucleotides of sequences SEQ ID NO 48-71, specific for the control cytosines, and combinations thereof. In a preferred embodiment, the kit of the present invention includes: oligonucleotides of specific type for the methodology employed, for the combination of cytosines of Panel WNT-SHH and / or Panel G3-G4, oligonucleotides specific for cytosines reference positive control / negative, a master mix containing a thermostable Taq polymerase, a suitable buffer and MgC at optimal concentrations, in addition to the optimized dNTPs for the methodology.
Finalmente, en otro aspecto principal, la presente invención contempla el conjunto de oligonucleótidos, de secuencias SEQ ID NO 1-79, diseñados para su empleo en el análisis de los niveles de metilación de las citosinas del panel WNT-SHH y/oFinally, in another main aspect, the present invention contemplates the set of oligonucleotides, of sequences SEQ ID NO 1-79, designed for use in the analysis of the levels of methylation of the cytosines of the WNT-SHH panel and / or
G3-G4. G3-G4.
EJEMPLOS Ejemplo 1 EXAMPLES Example 1
Identificación del panel de citosinas con metilación diferencial capaces de discriminar los subgrupos moleculares de meduloblastoma. Identification of the cytosine panel with differential methylation capable of discriminating the molecular subgroups of medulloblastoma.
El estudio partió de la hipótesis de que existen patrones de metilación diferencial entre los subgrupos moleculares de meduloblastoma (MB) con claras diferencias de comportamiento clínico o biológicamente distintos, y que estos perfiles de metilación son susceptibles de poder representar un marcador molecular de clasificación en pacientes con MB. The study was based on the hypothesis that there are differential methylation patterns between the molecular subgroups of medulloblastoma (MB) with clear differences in clinical or biologically distinct behavior, and that these methylation profiles are likely to represent a molecular classification marker in patients with MB.
Patrones de metilación Methylation patterns
En primer lugar se realizó un análisis de los patrones de metilación del ADN de un total de 106 meduloblastomas primarios obtenidos en fresco y/o FF en el momento del diagnóstico. Los datos de metilación del ADN fueron obtenidos mediante tecnología de microarray de alta densidad (lllumina Human Methylation BeadChip 450K, HM450K). Estos datos de metilación fueron generados en el contexto de estudios genómicos que han identificado y descrito la presencia de cuatro subgrupos moleculares principales de MB: wingless (WNT), Sonic hedgehog (SHH), Grupo 3 y Grupo 4.  First, an analysis of the DNA methylation patterns of a total of 106 primary medulloblastomas obtained in fresh and / or FF at the time of diagnosis was performed. DNA methylation data were obtained using high density microarray technology (Human Methylation BeadChip 450K, HM450K). These methylation data were generated in the context of genomic studies that have identified and described the presence of four major molecular subgroups of MB: wingless (WNT), Sonic hedgehog (SHH), Group 3 and Group 4.
Como parte de la validación de los resultados se realizaron estudios comparativos con diversos tumores del SNC y tejidos normales, utilizando diversas bases de datos de metilación generadas mediante HM450k. Los datos genómicos de metilación del ADN utilizados en el estudio, junto a datos clínico-biológicos y clasificación molecular de las muestras, se encuentran disponibles en el repositorio público del National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (www.ncbi.nlm.nih.gov/gds). Las bases de datos utilizadas se muestran en las Tablas 5 y 6. As part of the validation of the results, comparative studies were performed with various CNS tumors and normal tissues, using various methylation databases generated by HM450k. The genomic DNA methylation data used in the study, together with clinical-biological data and molecular classification of the samples, are available in the public repository of the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (www. ncbi.nlm.nih.gov/gds). The databases used are shown in Tables 5 and 6.
Tabla 5. Bases de datos de Meduloblastoma empleadas en el estudio Table 5. Medulloblastoma databases used in the study
GEO* GEO*
Título Muestras Enlace  Title Samples Link
ID  ID
Microarray-based DNA  Microarray-based DNA
methylation profiling of https:/ www.ncbi.nlm.nih. methylation profiling of https: / www.ncbi.nlm.nih.
GSE5 GSE5
medulloblastoma and 106 qov/qeo/query/acc.cqi?a 4880  medulloblastoma and 106 qov / qeo / query / acc.cqi? a 4880
normal cerebellum cc=GSE54880  normal cerebellum cc = GSE54880
samples Microarray-based DNA samples Microarray-based DNA
methylation profiling of https://mvw.ncbLnlrn.nih, methylation profiling of https: //mvw.ncbLnlrn.nih,
GSE5 GSE5
medulloblastoma and 169 Q o v/q eo/q u e rv/a cc . cq i ? a medulloblastoma and 169 Q or v / q eo / q u e rv / a cc. cq i? to
4880 4880
normal cerebellum cc=GSE54880  normal cerebellum cc = GSE54880
samples  samples
*Gene Expression Omnibus (GEO): repositorio de bases de datos genómicas. https://wvvw.ncbi.nlrri.nih.gov/geo  * Gene Expression Omnibus (GEO): repository of genomic databases. https://wvvw.ncbi.nlrri.nih.gov/geo
Tabla 6. Bases de datos de tumores y tejidos normales empleadas en el estudio Table 6. Databases of tumors and normal tissues used in the study
GEO ID Título Muestras Enlace GEO ID Title Samples Link
Recurrent Variations in  Recurrent Variations in
DNA Methylation in https://www.ncbi.nlm.  DNA Methylation in https: //www.ncbi.nlm.
GSE30  GSE30
Human Pluripotent Stem 40 nih.gov/geo/query/ac 654  Human Pluripotent Stem 40 nih.gov/geo/query/ac 654
Cells and their c.cqi?acc=GSE3G854 Differentiated Derivatives  Cells and their c.cqi? Acc = GSE3G854 Differentiated Derivatives
Methylation data from https://www.ncbi.nim.  Methylation data from https: //www.ncbi.nim.
GSE36  GSE36
glioblastoma tumor 126 nih.gov/geo/query/ac 278  glioblastoma tumor 126 nih.gov/geo/query/ac 278
samples c.cqi?aee=GSE36278 samples c.cqi? aee = GSE36278
DNA methylation data DNA methylation data
https://www.ncbi.nlm.  https: //www.ncbi.nlm.
GSE44 from pilocytic astrocytoma  GSE44 from pilocytic astrocytoma
54 n i h . g o v/g e o/q u e ry/a c 684 tumor samples and normal  54 n i h. g o v / g e o / q u e ry / a c 684 tumor samples and normal
c.cgi?acc=GSE44684 cerebellum controls  c.cgi? acc = GSE44684 cerebellum controls
Epigenomic Alterations  Epigenomic Alterations
https://www.ncbi.nlm.  https: //www.ncbi.nlm.
GSE45 Define Lethal CIMP- GSE45 Define Lethal CIMP-
48 nih.gov/geo/query/ac 353 positive Ependymomas of 48 nih.gov/geo/query/ac 353 positive Ependymomas of
c.cgi?acc=GSE45353 Infancy  c.cgi? acc = GSE45353 Infancy
/Ilumina Infinium 450K https://www.ncbi.nlm.  / Illuminates Infinium 450K https: //www.ncbi.nlm.
GSE50  GSE50
array data for Diffuse 24 nih.gov/geo/query/ac 022  array data for Diffuse 24 nih.gov/geo/query/ac 022
Intrinsic Pontine Glioma c.cgi?acc=GSE50022 Intrinsic Pontine Glioma c.cgi? Acc = GSE50022
Differences in DNA https ://www. ncbi . n I m ,Differences in DNA https: // www. ncbi. n I m,
GSE50 GSE50
methylation between 24 nih.gov/geo/query/ac 798  methylation between 24 nih.gov/geo/query/ac 798
human neurona! and glial c.cqi?acc=GSE50798 cells are concentrated in human neuron! and glial c.cqi? acc = GSE50798 cells are concentrated in
enhancers and non-CpG  enhancers and non-CpG
sites  sites
DNA methylation changes  DNA methylation changes
at CpG and non-CpG sites https://www.ncbi.nlm.  at CpG and non-CpG sites https: //www.ncbi.nlm.
GSE54  GSE54
are associated with 41 nih.qov/qeo/query/ac 719  are associated with 41 nih.qov / qeo / query / ac 719
development and clinical c.cc¡i?acc=GSE547 9 behaviorin neuroblastoma  development and clinical c.cc¡i? acc = GSE547 9 behaviorin neuroblastoma
Microarray-based DNA  Microarray-based DNA
methylation profiling of https://www.ncbi.nim.  methylation profiling of https: //www.ncbi.nim.
GSE54  GSE54
medulloblastoma and 8 nih.qov/qeo/query/ac 880  medulloblastoma and 8 nih.qov / qeo / query / ac 880
normal cerebellum c.cqi?acc=GSE54830 samples  normal cerebellum c.cqi? acc = GSE54830 samples
Genome wide methylation https://www.ncbi.nlm.  Genome wide methylation https: //www.ncbi.nlm.
GSE55  GSE55
profliling of pediatric 98 nih.qov/qeo/query/ac 712  profliling of pediatric 98 nih.qov / qeo / query / ac 712
glioblastomas c.cqi?acc=GSE557 2 glioblastomas c.cqi? acc = GSE557 2
Comprehensive genomic https ://www. ncbi . n I m ,Comprehensive genomic https: // www. ncbi. n I m,
GSE65 GSE65
analysis of relapse 14 nih.gov/geo/query/ac 306  analysis of relapse 14 nih.gov/geo/query/ac 306
neuroblastoma c.cqj?acc=GSE653G8 neuroblastoma c.cqj? acc = GSE653G8
The genomic and The genomic and
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150 nih.qov/qeo/query/ac 460 atypical teratoid rhabdoid  150 nih.qov / qeo / query / ac 460 atypical teratoid rhabdoid
c.cqi?acc=GSE70460 tumors  c.cqi? acc = GSE70460 tumors
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https://www.ncbi.nlm.  https: //www.ncbi.nlm.
GSE70 reprogramming in human  GSE70 reprogramming in human
7 nih.qov/qeo/query/ac 737 isogenic system identified  7 nih.qov / qeo / query / ac 737 isogenic system identified
c.cqi?acc=GSE70737 a clone selection criterion  c.cqi? acc = GSE70737 a clone selection criterion
*Gene Expression Omnibus (GEO): repositorio de bases de datos genómicas. https://www.ncbi.nlm.nih.qov/qeo  * Gene Expression Omnibus (GEO): repository of genomic databases. https: //www.ncbi.nlm.nih.qov/qeo
El estudio realizado partió de los datos genómicos brutos (archivos denominados Intensity Data files - ¡Dat) incluidos en la base de datos GSE54880, generados a partir de un total de 106 meduloblastomas (cohorte de estudio) primarios obtenidos en fresco en el momento del diagnóstico (Tabla 5). A partir de los archivos ¡Dat de la cohorte de estudio se generó una única base de datos. A continuación, se procedió con la normalización, el control de calidad y filtrado de los datos de metilación, según se ha descrito previamente (Gómez S et al. DNA methylation fingerprint of neuroblastoma reveáis new biológica! and clinical insights. GenomicsThe study was based on raw genomic data (files called Intensity Data files - ¡Dat) included in the GSE54880 database, generated from a total of 106 primary medulloblastomas (study cohort) obtained fresh at the time of diagnosis (Table 5). From the Dat files of the study cohort, a single database was generated. Next, we proceeded with the normalization, quality control and filtering of the methylation data, as previously described (Gómez S et al. DNA methylation fingerprint of neuroblastoma reveal new biological! And clinical insights. Genomics
Data 2015, 5: 360-363 Gómez S et al. DNA methylation fingerprint of neuroblastoma reveáis new biológica! and clinical insights. Epigenomics 2015: 1- 17; Kulis et al., Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia. Nature Genetics 2012, 44(11): 1236-1242). Para ello se utilizaron paquetes de herramientas de bioinformática disponibles a través de R/Bioconductor (http://www.bioconductor.org/). Data 2015, 5: 360-363 Gómez S et al. DNA methylation fingerprint of neuroblastoma reveal new biological! and clinical insights. Epigenomics 2015: 1-17; Kulis et al., Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia. Nature Genetics 2012, 44 (11): 1236-1242). For this, bioinformatics tool packages available through R / Bioconductor (http://www.bioconductor.org/) were used.
La calidad de las muestras fue evaluada utilizando el logaritmo de la mediana de las diversas capturas de intensidad de metilación. Se procedió con un análisis de la distribución de la dispersión de los datos brutos mediante density plots (diagrama de distribución de la variabilidad) (Figura 1A) y scatter plots (diagrama de dispersión) (Figura 1 B) con el fin de identificar muestras con valores de metilación que se alejan respecto a la media. La normalización se llevó a cabo utilizando la función SWAN (subset-quantile within array normalization) en el contexto de la metodología de normalización específica para el microarray HM450k, que puede obtenerse de los paquetes minfi (Aryee MJ. et al. Bioinformatics 2014; 30(10), 1363-1369) y ChAMP (Morris TJ et al. ChAMP: 450k Chip Analysis Methylation Pipeline. Bioinformatics 2014, 30(3):428-430; Morris TJ et al. The ChAMP Package (2016) Human Methylation EPIC Analysis www.bioconductor.org/packages/devel/bioc/vignettes/ChAMP/inst/'doc/ChAMP.pdf); Butcher LM and Beck S Probe Lasso: A novel method to rope in differentially methylated regions with 450K DNA methylation data. Methods 2015, 72, pp. 21- 28. doi: 10. 1016) entre otros (Figura 1 C). Sample quality was evaluated using the logarithm of the median of the various methylation intensity captures. We proceeded with an analysis of the distribution of the raw data dispersion using density plots (variability distribution diagram) (Figure 1A) and scatter plots (dispersion diagram) (Figure 1 B) in order to identify samples with methylation values that move away from the average. Normalization was carried out using the SWAN (subset-quantile within array normalization) function in the context of the specific standardization methodology for the HM450k microarray, which can be obtained from the minfi packages (Aryee MJ. Et al. Bioinformatics 2014; 30 (10), 1363-1369) and ChAMP (Morris TJ et al. ChAMP: 450k Chip Analysis Methylation Pipeline. Bioinformatics 2014, 30 (3): 428-430; Morris TJ et al. The ChAMP Package (2016) Human Methylation EPIC Analysis www.bioconductor.org/packages/devel/bioc/vignettes/ChAMP/inst/'doc/ChAMP.pdf); Butcher LM and Beck S Probe Lasso: A novel method to rope in differentially methylated regions with 450K DNA methylation data. Methods 2015, 72, pp. 21-28. Doi: 10. 1016) among others (Figure 1 C).
A partir de este punto se procedió con el filtrado de los datos. Para ello se utilizó un "pipeline" (cadena de elementos de procesamiento) que incluyó diversos filtros, con el fin de evitar un sesgo (Kulis et al., Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia. Nature Genetics 2012, 44(11):1236-1242; Gomez S et al. DNA methylation fingerprint of neuroblastoma reveáis new biológica! and clinical insights. Genomics Data 2015, 5: 360-363 Gomez S et al. DNA methylation fingerprint of neuroblastoma reveáis new biológica! and clinical insights. Epigenomics 2015: 1-17). Las citosinas con valores de detección con un valor P > 0.01 en más del 10% de las muestras, así como aquellos datos de metilación asociados a la impronta de metilación establecida de manera sexo-específica, fueron excluidas de la base de datos inicial (485,512 citosinas por cada muestra). Los restantes valores (n=475.038 CpG) constituyeron la base de datos de partida para el estudio. From this point the data was filtered. For this, a "pipeline" (chain of processing elements) was used that included various filters, in order to avoid bias (Kulis et al., Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia. Nature Genetics 2012, 44 (11): 1236-1242; Gomez S et al. DNA methylation fingerprint of neuroblastoma reveal new biological! and clinical insights. Genomics Data 2015, 5: 360-363 Gomez S et al. DNA methylation fingerprint of neuroblastoma reveal new biological! and clinical insights. Epigenomics 2015: 1-17). Cytosines with detection values with a P value> 0.01 in more than 10% of the samples, as well as those methylation data associated with the sex-specific methylation imprint, were excluded from the initial database (485,512 cytosines for each sample). The remaining values (n = 475.038 CpG) constituted the starting database for the study.
Tras la normalización y filtrado de los datos se procedió con el análisis de los patrones de metilación del ADN mediante el método estadístico multivariante no supervisado denominado Análisis de Componentes Principales (ACP). After the normalization and filtering of the data, the DNA methylation patterns were analyzed using the unsupervised multivariate statistical method called Principal Component Analysis (ACP).
El Análisis de Componentes Principales es una técnica matemática lineal de síntesis de la información, o reducción de la dimensión de un conjunto de datos (número de variables). Es decir, ante una base de datos con muchas variables (en este caso, listas de citosinas con distintos estados de metilación), el objetivo será reducirlas a un menor número perdiendo la menor cantidad de información posible.Principal Component Analysis is a linear mathematical technique of information synthesis, or reduction of the size of a data set (number of variables). That is, before a database with many variables (in this case, lists of cytosines with different states of methylation), the objective will be to reduce them to a smaller number by losing as little information as possible.
Los nuevos componentes principales o factores serán una combinación lineal de las variables originales, y además serán independientes entre sí. The new main components or factors will be a linear combination of the original variables, and will also be independent of each other.
Un aspecto clave en ACP es la interpretación de los factores, ya que ésta no viene dada a priori, sino que será deducida tras observar la relación de los factores con las variables iniciales. Al tratarse de un método estadístico no-supervisado el ACP no tiene en cuenta las variables clínicas. A key aspect in ACP is the interpretation of the factors, since this is not given a priori, but will be deduced after observing the relationship of the factors with the initial variables. As it is a non-supervised statistical method, the ACP does not take into account the clinical variables.
Un análisis de componentes principales tiene sentido si existen altas correlaciones entre las variables, ya que esto es indicativo de que existe información redundante y, por tanto, pocos factores explicarán gran parte de la variabilidad total. An analysis of main components makes sense if there are high correlations between the variables, since this is indicative of the fact that there is redundant information and, therefore, few factors will explain much of the total variability.
Se llevó a cabo un análisis de la distribución de la variabilidad (densisty plof) de los niveles de metilación de ADN en las muestras, con el fin de identificar las citosinas con mayor variabilidad y por tanto más significativas. Se identificaron 5.904 citosinas (1 ,2% de la totalidad de la citosinas estudiadas) aplicando una desviación estándar mayor o igual a 0,30 (Figura 2A). El análisis mediante ACP muestra como los niveles de metilación de las 5.904 citosinas seleccionadas (SD≥0,3) reagrupan las muestras de MB en subgrupos distintos; dos subgrupos claramente diferenciados y distanciados de los otros dos localizados más adyacentes entre ellos (Figura 2B). Asimismo, se aplicó otro método no supervisado denominado de clusteringAn analysis of the distribution of the variability (densisty plof) of the DNA methylation levels in the samples was carried out, in order to identify the cytosines with greater variability and therefore more significant. 5,904 cytosines (1.2% of all cytosines studied) were identified by applying a standard deviation greater than or equal to 0.30 (Figure 2A). The ACP analysis shows how the methylation levels of the 5,904 selected cytosines (SD≥0.3) regroup the MB samples in different subgroups; two subgroups clearly differentiated and distanced from the other two located more adjacent to each other (Figure 2B). Also, another unsupervised method called clustering was applied
(agrupación) jerárquico. El clustering no supervisado es un conjunto de técnicas que reagrupan los datos en función de una distancia sin utilizar ningún tipo de información externa para organizar los grupos. El clustering jerárquico es un método basado en una matriz de distancias. Establece grupos de condiciones que tienen un patrón común/similar y construye un dendrograma (representación gráfica de un grupo de relaciones basada en la cercanía o similitud de los datos). El dendrograma establece una relación ordenada de los grupos previamente definidos y la longitud de sus ramas es una representación de la distancia entre los distintos nodos del mismo. (grouping) hierarchical. Unsupervised clustering is a set of techniques that regroup data based on a distance without using any external information to organize the groups. Hierarchical clustering is a method based on a distance matrix. It establishes groups of conditions that have a common / similar pattern and constructs a dendrogram (graphical representation of a group of relationships based on the proximity or similarity of the data). The dendrogram establishes an ordered relationship of the previously defined groups and the length of its branches is a representation of the distance between the different nodes of the same.
Para el análisis de clustering jerárquico no supervisado, se utilizaron los niveles de metilación de las citosinas con SD≥0,3 (5.904 citosinas). De forma similar a los resultados obtenidos mediante ACP, el dendrograma generado por el clustering jerárquico mostró cuatro subgrupos con patrones de metilación diferencial, siendo dos de ellos más similares y heterogéneos entre ellos (Figura 2C). For the analysis of unsupervised hierarchical clustering, the levels of cytosine methylation with SD≥0.3 (5,904 cytosines) were used. Similar to the results obtained by ACP, the dendrogram generated by the hierarchical clustering showed four subgroups with differential methylation patterns, two of them being more similar and heterogeneous among them (Figure 2C).
Tras un análisis comparativo entre los subgrupos generados mediante ACP y clustering jerárquico y los datos clínico-biológicos disponibles, se corroboró como los subgrupos de muestras se asociaban de forma significativa con los subgrupos moleculares descritos previamente: WNT, SHH, Grupo 3 y Grupo 4 (Figura 2C). After a comparative analysis between the subgroups generated by ACP and hierarchical clustering and the available clinical-biological data, it was confirmed that the subgroups of samples were significantly associated with the molecular subgroups previously described: WNT, SHH, Group 3 and Group 4 ( Figure 2C).
A continuación los autores de la invención ahondaron en el análisis de los datos de metilación de las 5.904 citosinas más significativas, con el fin de perfilar los patrones de metilación diferencial y conseguir reducirlos a un menor número de citosinas perdiendo la menor cantidad de información posible. El objetivo principal era reducir la información de metilación del ADN redundante para identificar un patrón de metilación compuesto de pocos factores (citosinas) que explicarán gran parte de la variabilidad total del MB. The authors of the invention then delved into the analysis of the methylation data of the 5,904 most significant cytosines, in order to profile the differential methylation patterns and reduce them to a lower number of cytosines, losing as little information as possible. The main objective was to reduce the redundant DNA methylation information to identify a methylation pattern composed of few factors (cytosines) that will explain much of the total variability of the MB.
Partiendo del análisis no supervisado (SD≥0,3) de 5.904 citosinas en 106 muestras F/FF de MB, se seleccionaron aquellas citosinas que cumplían los siguientes criterios: 1) SD menor de 0, 1 entre las citosinas del mismo subgrupo de MB y 2) el promedio de cada subgrupo con mayor diferencia con los otros subgrupos de interés. Based on the unsupervised analysis (SD≥0.3) of 5,904 cytosines in 106 F / FF samples of MB, those cytosines that met the following criteria were selected: 1) SD less than 0, 1 among the cytosines of the same subgroup of MB and 2) the average of each subgroup with the greatest difference with the other subgroups of interest.
A partir de los patrones de metilación de las 5.904 citosinas, se identificaron dos conjuntos de citosinas que cumplían los criterios de selección deseados. A partir de estos patrones de metilación, se seleccionó un primer panel de nueve citosinas diferencialmente metiladas, con un patrón de metilación diferencial que se asoció de forma significativa y precisa con cada uno de los subgrupos moleculares definidos de acuerdo a la clasificación de los tumores del sistema nervioso central de la OMS (2016): WNT, SHH y Grupo no-WNT/no-SHH. From the methylation patterns of the 5,904 cytosines, two sets of cytosines were identified that met the desired selection criteria. From these methylation patterns, a first panel of nine differentially methylated cytosines was selected, with a differential methylation pattern that was significantly and accurately associated with each of the molecular subgroups defined according to the tumor classification of the WHO central nervous system (2016): WNT, SHH and Non-WNT / non-SHH Group.
El análisis mediante ACP utilizando únicamente las nueve citosinas seleccionadas, mostró la capacidad de estas citosinas de distinguir los tres subgrupos de MBs de forma similar a las 5.904 citosinas (Figura 3A). De esta forma se demostró que este panel de nueve citosinas (Panel WNT-SHH) es eficaz para establecer las tres entidades genéticas WNT, SHH, y no-WNT/no- SHH y puede representar un marcador de clasificación útil en pacientes con MB. The ACP analysis using only the nine selected cytosines showed the ability of these cytosines to distinguish the three subgroups of MBs in a similar way to the 5,904 cytosines (Figure 3A). In this way it was demonstrated that this nine cytosine panel (WNT-SHH Panel) is effective in establishing the three genetic entities WNT, SHH, and non-WNT / non-SHH and can represent a useful classification marker in patients with MB.
Cada subgrupo se asoció de forma específica y univoca con un patrón de metilación diferencial de las citosinas del Panel WNT-SHH. Cada citosina mostró un patrón de metilación bimodal especifico (niveles muy elevados de metilación; promedio valor metilación≥ 80%), o al contrario, niveles muy bajos; promedio valor metilación≤ 17%) para cada uno de los subgrupos, tal y como se recoge en la Tabla 7 y Figura 3B. Tabla 7. Ejemplo del patrón y porcentaje de metilacion de las citosinas del panel WNT-SHH en la cohorte de estudio F/FF (n=106) Each subgroup was specifically and univocally associated with a differential methylation pattern of the cytosines of the WNT-SHH Panel. Each cytosine showed a specific bimodal methylation pattern (very high levels of methylation; average methylation value ≥ 80%), or conversely, very low levels; average methylation value ≤ 17%) for each of the subgroups, as shown in Table 7 and Figure 3B. Table 7. Example of the pattern and percentage of methylation of the cytosines of the WNT-SHH panel in the F / FF study cohort (n = 106)
Figure imgf000037_0001
Figure imgf000037_0001
Aquellos tumores con un patrón de metilacion con valores elevados en las citosinas WNT1_MB y WNT2_MB y niveles bajos de metilacion en WNT3_MB, se asociaron de forma específica e univoca con el subgrupo WNT de MBs. Cuando se observaron valores de metilacion elevados en las citosinas SHH1_MB y SHH2_MB y bajos en SHH3_MB, este patrón definía de forma univoca y directa el subgrupo SHH. Valores elevados en N-WS1_MB y N-WS2_MB, y bajos en N- WS3_MB era indicador de un tumor que pertenece al subgrupo no-WNT/no-SHH de MB (Tabla 7 y Figura 3B). Con el fin de investigar la capacidad de clasificación del panel de nueve citosinas, se procedió con la determinación del subgrupo molecular de la cohorte de estudio de 106 MBs según el patrón de metilacion del Panel WNT-SHH (Tabla 7). Se realizó el análisis de los datos de metilacion mediante un Análisis Discriminante. El Análisis Discriminante Lineal (LDA, del inglés Lineal Discriminant Analysis) es una técnica estadística que permite identificar las características que diferencian (discriminan) a dos o más grupos y a crear una función capaz de distinguir con mayor precisión posible los miembros de dos o más grupos. El LDA permite identificar qué variables permiten diferenciar a los grupos y cuántas de estas variables son necesarias para alcanzar la mejor clasificación posible. La pertenencia a los grupos, conocida de antemano, se utiliza como variable dependiente (variable categórica). Las variables (variables continuas) que diferencian los grupos se utilizan como variables de clasificación (variables discriminantes). Those tumors with a methylation pattern with high values in the cytosines WNT1_MB and WNT2_MB and low levels of methylation in WNT3_MB, were specifically and uniquely associated with the WNT subgroup of MBs. When high methylation values were observed in the SHH1_MB and SHH2_MB cytosines and low in SHH3_MB, this pattern defined the SHH subgroup univocally and directly. High values in N-WS1_MB and N-WS2_MB, and low in N-WS3_MB was an indicator of a tumor belonging to the non-WNT / non-SHH subgroup of MB (Table 7 and Figure 3B). In order to investigate the classification capacity of the nine cytosine panel, the molecular subgroup of the study cohort of 106 MBs was determined according to the methylation pattern of the WNT-SHH Panel (Table 7). Methylation data analysis was performed using a Discriminant Analysis. Linear Discriminant Analysis (LDA) is a statistical technique that identifies the characteristics that differentiate (discriminate) two or more groups and create a function capable of distinguishing with as accurately as possible the members of two or more groups. The LDA allows to identify which variables allow to differentiate the groups and how many of these variables are necessary to achieve the best possible classification. Group membership, known in advance, is used as a dependent variable (categorical variable). Variables (continuous variables) that differentiate groups are used as classification variables (discriminant variables).
El análisis discriminante se realizó mediante la función LDA contenida en el paquete MASS (Modern Applied Statistics with S, Venables and Ripley, 2002) en RThe discriminant analysis was performed using the LDA function contained in the MASS package (Modern Applied Statistics with S, Venables and Ripley, 2002) in R
(https://cran.r-project.org/), según se ha descrito previamente (Queirós AC et al. Leukemia (2015) 29,598-605). (https://cran.r-project.org/), as previously described (Queirós AC et al. Leukemia (2015) 29,598-605).
Se utilizaron los valores de metilación de las nueve citosinas del Panel WNT-SHH de la cohorte de estudio para entrenar la función LDA y generar un modelo LDA de clasificación. Se aplicó la función LDA también para testar todas las posibles combinaciones (29 combinaciones) para definir qué citosinas y cuántas de éstas eran necesarias para obtener la mejor clasificación posible. Tanto las nueve citosinas como todas las posibles combinaciones (29 combinaciones) permitieron clasificar la totalidad de las muestras de la cohorte y se observó una concordancia del 100% entre la clasificación realizada con las diversas combinaciones del Panel WNT-SHH y los datos publicados previamente con la misma cohorte de MB. Esto resultados demuestran como diversas combinaciones de estas citosinas tienen la capacidad de clasificar correctamente los MBs y que dichas combinaciones son susceptibles de representar potenciales marcadores adecuados para la clasificación de estos tumores. The methylation values of the nine cytosines of the WNT-SHH Panel of the study cohort were used to train the LDA function and generate an LDA classification model. The LDA function was also applied to test all possible combinations (2 9 combinations) to define which cytosines and how many of these were necessary to obtain the best possible classification. Both the nine cytosines and all possible combinations (2 9 combinations) allowed to classify the totality of the samples of the cohort and a concordance of 100% was observed between the classification made with the various combinations of the WNT-SHH Panel and the previously published data with the same cohort of MB. These results demonstrate how various combinations of these cytosines have the ability to correctly classify MBs and that such combinations are likely to represent potential markers suitable for the classification of these tumors.
Posteriormente se evaluó la especificidad del patrón de metilación. Un análisis comparativo de los valores de metilación de las nueve citosinas en otros tumores y tejido humanos normales, mostró una elevada especificidad del patrón de metilación del Panel WNT-SHH para MB (Figura 3C). Subsequently, the specificity of the methylation pattern was evaluated. A comparative analysis of the methylation values of the nine cytosines in other normal human tumors and tissue showed a high specificity of the WNT-SHH Panel methylation pattern for MB (Figure 3C).
Para identificar el segundo patrón de metilación de ADN que se asocia de forma significativa y específica con los subgrupos Grupo 3 y Grupo 4 se aplicaron los siguientes criterios de selección: 1) SD menor de 0, 1 entre las citosinas del mismo subgrupo de MB y 2) el promedio de cada subgrupo con mayor diferencia con el otro subgrupo, obteniendo el patrón de metilación diferencial de 8 citosinas que constituyen el Panel G3-G4 (Tabla 8). To identify the second DNA methylation pattern that is associated in a way significant and specific with the subgroups Group 3 and Group 4 the following selection criteria were applied: 1) SD less than 0, 1 among the cytosines of the same subgroup of MB and 2) the average of each subgroup with greater difference with the other subgroup , obtaining the differential methylation pattern of 8 cytosines that constitute Panel G3-G4 (Table 8).
Para aquellos MBs clasificados como no-WNT/no-SHH con el Panel WNT-SHH de nueve citosinas, se contempló a continuación el empleo conjunto de los niveles de metilación del Panel G3-G4 para poder distinguir y clasificar los tumores no- WNT/no-SHH en Grupo 3 o Grupo 4. For those MBs classified as non-WNT / non-SHH with the WNT-SHH Panel of nine cytosines, the joint use of the methylation levels of Panel G3-G4 to distinguish and classify non-WNT / tumors was then contemplated. non-SHH in Group 3 or Group 4.
El análisis mediante PCA mostró la capacidad del Panel G3-G4 de discriminar el Grupo 3 y el Grupo 4 de forma significativa (Figura 4A). El Panel G3-G4 es susceptible de poder representar un marcador útil para la clasificación de MBs pertenecientes a las entidades genéticas Grupo 3 y Grupo 4 (Tabla 8). The PCA analysis showed the ability of Panel G3-G4 to discriminate Group 3 and Group 4 significantly (Figure 4A). Panel G3-G4 is capable of representing a useful marker for the classification of MBs belonging to the genetic entities Group 3 and Group 4 (Table 8).
Aquellos tumores con un patrón de metilación con valores elevados (≥ 75%) en las citosinas Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB y Gr4-D_MB, se asocian de forma específica y unívoca con el subgrupo Grupo 3 de MBs. Mientras que valores bajos en las citosinas Gr3-A_MB, Gr3- B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB y Gr4-D_MB es indicador de un tumor que pertenece al subgrupo Grupo 4 (Tabla 8 y Figura 4B). Those tumors with a methylation pattern with high values (≥ 75%) in the cytosines Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB, they are specifically and uniquely associated with the subgroup Group 3 of MBs. While low values in cytosines Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB is an indicator of a tumor belonging to the Group 4 subgroup ( Table 8 and Figure 4B).
Tabla 8. Ejemplo del patrón y porcentajes de metilación de las citosinas del panel G3-G4 en la cohorte de estudio F/FF (n=106) Table 8. Example of the pattern and percentages of cytosine methylation of the G3-G4 panel in the F / FF study cohort (n = 106)
G3 G4 G3 G4
Gr3-A_MB 82% 26%  Gr3-A_MB 82% 26%
Gr3-B_MB 84% 22%  Gr3-B_MB 84% 22%
Gr-3-C_MB 79% 18%  Gr-3-C_MB 79% 18%
Gr-3-D_MB 86% 28%  Gr-3-D_MB 86% 28%
Gr-4-A_MB 66% 14% Gr-4-B_MB 72% 19% Gr-4-A_MB 66% 14% Gr-4-B_MB 72% 19%
Gr-4-C_MB 72% 7%  Gr-4-C_MB 72% 7%
Gr-4-D_MB 78% 27%  Gr-4-D_MB 78% 27%
Del mismo modo que con el Panel WNT-SHH, se procedió con el desarrollo de un modelo LDA de predicción de los subgrupos 3 y 4 utilizando los datos de metilación de la cohorte de estudio de las citosinas de Panel G3-G4. Asimismo se testaron todas las posibles combinaciones (28 combinaciones) para definir qué citosinas y cuántas de estas son necesarias para obtener la mejor clasificación posible. Tanto las ocho citosinas, como sus posibles combinaciones, permitieron clasificar la totalidad de las muestras de la cohorte y se observó una concordancia del 100% entre la clasificación realizada con las diversas combinaciones del Panel G3-G4 y los datos publicados previamente con la misma cohorte de MB. Esto resultados demuestran como diversas combinaciones de estas citosinas tienen la capacidad de clasificar correctamente los MBs y que dichas combinaciones son susceptibles de representar potenciales marcadores adecuados para la clasificación de estos tumores. In the same way as with the WNT-SHH Panel, we proceeded with the development of an LDA model for prediction of subgroups 3 and 4 using the methylation data of the cohort of study of Panel G3-G4 cytosines. Likewise, all possible combinations (2 8 combinations) were tested to define which cytosines and how many of these are necessary to obtain the best possible classification. Both the eight cytosines, as well as their possible combinations, allowed the classification of the totality of the cohort samples and a 100% concordance was observed between the classification made with the different combinations of Panel G3-G4 and the data previously published with the same cohort. of MB. These results demonstrate how various combinations of these cytosines have the ability to correctly classify MBs and that such combinations are likely to represent potential markers suitable for the classification of these tumors.
De forma similar al Panel WNT-SHH, el Panel G3-G4 mostró una elevada especificidad del patrón de metilación de las ocho citosinas en MB en comparación con los valores de metilación en otros tumores y tejido humanos normales (Figura 4C). Similar to the WNT-SHH Panel, Panel G3-G4 showed a high specificity of the methylation pattern of the eight cytosines in MB compared to methylation values in other normal human tumors and tissue (Figure 4C).
Conclusión ejemplo 1: Conclusion example 1:
Existen perfiles de metilación del ADN específicos capaces de discriminar nítidamente entre subgrupos moleculares de MB. Estos patrones de metilación asociados con el comportamiento clínico de los tumores meduloblastoma, son susceptibles de poder representar un marcador molecular de estratificación que contribuya a una clasificación más precisa y rápida de los diferentes subtipos de MB.  There are specific DNA methylation profiles capable of clearly discriminating between molecular subgroups of MB. These methylation patterns associated with the clinical behavior of medulloblastoma tumors are likely to be able to represent a molecular stratification marker that contributes to a more precise and rapid classification of the different MB subtypes.
Ejemplo 2 Validación de un método de clasificación de Meduloblastoma utilizando bases de datos de microarrays de metilación del ADN. Example 2 Validation of a Medulloblastoma classification method using DNA methylation microarray databases.
A partir de este punto el objetivo del estudio fue comprobar si las citosinas seleccionadas para el Panel WNT-SHH y Panel G3-G4 eran eficaces para distinguir y clasificar las entidades genéticas descritas como WNT, SHH, Grupo 3 y From this point the objective of the study was to verify whether the cytosines selected for Panel WNT-SHH and Panel G3-G4 were effective in distinguishing and classifying the genetic entities described as WNT, SHH, Group 3 and
Grupo 4 en MB. Group 4 in MB.
Validación: base de datos de microarrays de MB primarios fijados en formalina e incluidos en parafina. Validation: database of primary MB microarrays fixed in formalin and included in paraffin.
Para ello, los autores de la invención partieron de una base de datos de metilación del ADN generada con una primera cohorte independiente de muestras (cohorte de validación) (n = 169; 15 WNT, 39 SHH, 42 G3 y 73 G4) de MB fijados en formalina e incluidos en parafina (FFPE) en el momento del diagnóstico. Los datos de metilación del ADN fueron obtenidos mediante tecnología de microarray de alta densidad {¡Ilumina HumanMethylation BeadChip450k, HM450K). Estos datos de metilación fueron generados en el contexto de estudios genómicos previamente publicados (Hovestadt V et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archiva! tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125:913-916). Dichos datos genómicos de metilación del ADN, junto a datos clínico-biológicos de las muestras, se encuentran disponibles en el repositorio público del National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (www.ncbi.nlm.nih.gov/gds); número de referencia de la base de datos: GSE54880. Las bases de datos utilizadas por los autores de la invención se muestran en la Tablas 5 y 6. For this, the inventors started from a DNA methylation database generated with a first independent sample cohort (validation cohort) (n = 169; 15 WNT, 39 SHH, 42 G3 and 73 G4) of MB fixed in formalin and included in paraffin (FFPE) at the time of diagnosis. DNA methylation data was obtained using high density microarray technology {Illuminates HumanMethylation BeadChip450k, HM450K). These methylation data were generated in the context of previously published genomic studies (Hovestadt V et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archives! Tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125: 913-916). These genomic DNA methylation data, together with clinical-biological data of the samples, are available in the public repository of the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (www.ncbi.nlm.nih.gov / gds); reference number of the database: GSE54880. The databases used by the authors of the invention are shown in Tables 5 and 6.
El estudio realizado por los autores de la invención partió de los datos genómicos brutos (archivos denominados ¡Dat files del inglés Intensity Data files) de la cohorte de validación (Tabla 5 y 6). A partir de los archivos ¡Dat se generó una única base de datos. A continuación, se procedió con la normalización, el control de calidad y filtrado de los datos de metilación, según los procedimientos descritos en el Ejemplo 1. The study carried out by the authors of the invention was based on the raw genomic data (files called Dat files of the English Intensity Data files) of the validation cohort (Table 5 and 6). A single database was generated from the ¡Dat files. Then, the normalization, quality control and filtering of the methylation data was carried out, according to the procedures described in Example 1.
A partir de este punto, se extrajeron los datos de metilación correspondientes a las citosinas incluidas en el Panel WNT-SHH y Panel G3-G4, y se procedió con el análisis de los patrones de metilación y con la comparación con los datos de los subgrupos moleculares. From this point, the methylation data corresponding to the cytosines included in Panel WNT-SHH and Panel G3-G4 were extracted, and the methylation patterns were analyzed and compared with the subgroups data molecular.
El análisis mediante ACP utilizando únicamente las nueve citosinas de la base de datos de validación correspondientes al Panel WNT-SHH, mostraban distribución de las muestras equivalente a la obtenida con la de la cohorte de estudio (Figura 3A y 5A). De forma similar, las citosinas de la cohorte de validación se asociaban de forma significativa y específica con las tres entidades genéticas definidas por la OMS (2016): WNT, SHH y Grupo no-WNT/no-SHH de MB (Tabla 9). The ACP analysis using only the nine cytosines from the validation database corresponding to the WNT-SHH Panel, showed distribution of the samples equivalent to that obtained with that of the study cohort (Figure 3A and 5A). Similarly, the cytosines of the validation cohort were significantly and specifically associated with the three genetic entities defined by WHO (2016): WNT, SHH and Non-WNT / non-SHH Group of MB (Table 9).
Similar al patrón original del Panel WNT-SHH (Tabla 7), en la cohorte de validación cada subgrupo se asoció de forma específica e univoca con un patrón de metilación bimodal diferencial de las citosinas (Tabla 9). Similar to the original WNT-SHH Panel pattern (Table 7), in the validation cohort each subgroup was specifically and univocally associated with a pattern of differential bimodal cytosine methylation (Table 9).
Tabla 9. Comparación del patrón y porcentaje de metilación del panel WNT-SHH en la base de datos de la cohorte de estudio F/FF (n=106) y la base de datos de la cohorte de validación FFPE (n=169) Table 9. Comparison of the pattern and methylation percentage of the WNT-SHH panel in the database of the F / FF study cohort (n = 106) and the FFPE validation cohort database (n = 169)
No-WNT/no- WNT SHH  No-WNT / no- WNT SHH
SHH  SHH
F/FF FFPE F/FF FFPE F/FF FFP  F / FF FFPE F / FF FFPE F / FF FFP
E AND
WNT1_MB 8% 1 1 % 9% 1 1 % 88% 79% WNT1_MB 8% 1 1% 9% 1 1% 88% 79%
WNT2_MB 2% 2% 2% 5% 85% 66%  WNT2_MB 2% 2% 2% 5% 85% 66%
WNT3_MB 92% 90% 91 % 90% 12% 21 % N-WS1_MB 89% 89% 16% 24% 17% 28% WNT3_MB 92% 90% 91% 90% 12% 21% N-WS1_MB 89% 89% 16% 24% 17% 28%
N-WS2_MB 80% 76% 1 1 % 16% 15% 20%  N-WS2_MB 80% 76% 1 1% 16% 15% 20%
N-WS3_MB 9% 12% 88% 87% 87% 83%  N-WS3_MB 9% 12% 88% 87% 87% 83%
SHH1_MB 14% 15% 90% 87% 13% 19%  SHH1_MB 14% 15% 90% 87% 13% 19%
SHH2_MB 6% 6% 88% 86% 18% 19%  SHH2_MB 6% 6% 88% 86% 18% 19%
SHH3_MB 96% 96% 1 1 % 13% 96% 95%  SHH3_MB 96% 96% 1 1% 13% 96% 95%
Con el fin de investigar la capacidad de clasificación del Panel WNT-SHH, se procedió con la determinación del subgrupo molecular de la cohorte de validación aplicando el modelo LDA de clasificación. Se observó como las citosinas discriminaban de forma clara y eran capaces de clasificar la totalidad de las muestras con un 100% de concordancia con los datos de clasificación publicados previamente con la misma cohorte de MB (Hovestadt V et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archiva! tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125:913-916). In order to investigate the classification capacity of the WNT-SHH Panel, the molecular subgroup of the validation cohort was determined by applying the LDA classification model. It was observed how the cytosines clearly discriminated and were able to classify all the samples with 100% concordance with the previously published classification data with the same MB cohort (Hovestadt V et al. Robust molecular subgrouping and copy- number profiling of medulloblastoma from small amounts of archival! tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125: 913-916).
A continuación, se analizaron las citosinas de las base de datos de validación correspondientes al Panel G3-G4. Dichas citosinas mostraron un patrón de metilación equivalente al patrón de la cohorte de estudio del Panel G3-G4, asociado de forma significativa y específica con los subgrupos Grupo 3 y Grupo 4 (Figura 5B y Tabla 10). Next, the cytosines of the validation databases corresponding to Panel G3-G4 were analyzed. These cytosines showed a methylation pattern equivalent to the pattern of the study cohort of Panel G3-G4, significantly and specifically associated with the subgroups Group 3 and Group 4 (Figure 5B and Table 10).
Tabla 10. Ejemplo del patrón y porcentaje de metilación del panel G3-G4 en la base de la cohorte de estudio F/FF (n=106) y la base de datos de la cohorte de validación FFPE (n=169) Table 10. Example of the pattern and percentage of methylation of the G3-G4 panel at the base of the F / FF study cohort (n = 106) and the FFPE validation cohort database (n = 169)
G3 G4  G3 G4
F/FF FFPE F/FF FFPE  F / FF FFPE F / FF FFPE
Gr3-A_MB 82% 82% 26% 33%  Gr3-A_MB 82% 82% 26% 33%
Gr3-B_MB 84% 79% 22% 24%  Gr3-B_MB 84% 79% 22% 24%
Gr-3-C_MB 79% 76% 18% 24%  Gr-3-C_MB 79% 76% 18% 24%
Gr-3-D_MB 86% 87% 28% 35%  Gr-3-D_MB 86% 87% 28% 35%
Gr-4-A_MB 66% 60% 14% 18% Gr-4-B_MB 72% 63% 19% 25% Gr-4-A_MB 66% 60% 14% 18% Gr-4-B_MB 72% 63% 19% 25%
Gr-4-C_MB 72% 61 % 7% 14%  Gr-4-C_MB 72% 61% 7% 14%
Gr-4-D_MB 78% 65% 27% 28%  Gr-4-D_MB 78% 65% 27% 28%
Para evaluar la capacidad de clasificación, se aplicó el modelo LDA de clasificación del Panel G3-G4 a las muestras clasificadas como no-WNT/no-SHH (n=1 15) por el Panel WNT-SHH. Se observó como el Panel G3-G4 era capaz de diferenciar de forma eficaz las dos entidades Grupo 3 y Grupo 4 con un 97% de concordancia (41/42 G3 y 71/73 G4) con los datos de clasificación publicados previamente con la misma cohorte de MB (Hovestadt V et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archiva! tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125:913-916). To assess the classification capacity, the LDA classification model of Panel G3-G4 was applied to samples classified as non-WNT / non-SHH (n = 1 15) by the WNT-SHH Panel. It was observed how Panel G3-G4 was able to effectively differentiate the two entities Group 3 and Group 4 with 97% concordance (41/42 G3 and 71/73 G4) with the classification data previously published with it MB cohort (Hovestadt V et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archiva! tumor material using high-density DNA methylation arrays. Acta Neuropathologica 2013, 125: 913-916).
Conclusiones Ejemplo 2: Conclusions Example 2:
Al utilizar diversas bases de datos de metilación del ADN, se demostró cómo las citosinas del Panel WNT-SHH son eficaces para establecer los subgrupos moleculares de las entidades genéticas WNT, SHH y Grupo no-WNT/no-SHH de MB.  Using various DNA methylation databases, it was demonstrated how the cytosines of the WNT-SHH Panel are effective in establishing the molecular subgroups of the WNT, SHH and non-WNT / non-SHH Group genetic entities of MB.
De esta forma se demostró también como el patrón de metilación diferencial de las citosinas que constituyen el Panel G3-G4 representa un marcador eficaz para la clasificación molecular de MBs pertenecientes a las entidades genéticas Grupo 3 y Grupo 4. In this way it was also demonstrated how the differential methylation pattern of the cytosines that constitute Panel G3-G4 represents an effective marker for the molecular classification of MBs belonging to the Group 3 and Group 4 genetic entities.
Asimismo, se demostró que el perfil de metilación de las citosinas de interés de tejido tumoral fijado en formalina e incluido en parafina es comparable al tejido obtenido en fresco y/o conservado congelado a -80°C. Por lo tanto, al mantenerse estable el patrón de metilación de las citosinas los marcadores propuestos, Panel WNT-SHH y Panel G3-G4, el método de clasificación propuesto es aplicable a este tipo de material biológico. Ejemplo 3 Validación de un método de clasificación de MB primarios utilizando diversas metodologías y cohortes independientes Likewise, it was demonstrated that the methylation profile of the cytosines of interest of tumor tissue fixed in formalin and included in paraffin is comparable to the tissue obtained in fresh and / or preserved frozen at -80 ° C. Therefore, by maintaining the cytosine methylation pattern the proposed markers, Panel WNT-SHH and Panel G3-G4, the proposed classification method is applicable to this type of biological material. Example 3 Validation of a primary MB classification method using various independent methodologies and cohorts
A partir de este punto el objetivo del estudio fue validar el análisis de las citosinas de interés (Panel WNT-SHH y Panel G3-G4) mediantes técnicas moleculares tales como la secuenciación por bisulfito (BSP) y la pirosecuenciación por bisulfito, u otras técnicas moleculares similares adecuadas para realizar un análisis del estado de metilación del ADN.  From this point, the objective of the study was to validate the analysis of the cytosines of interest (Panel WNT-SHH and Panel G3-G4) through molecular techniques such as bisulfite sequencing (BSP) and bisulfite pyrosequencing, or other techniques Similar molecular molecules suitable for analyzing the DNA methylation status.
Para ello utilizaron 108 muestras de MB primarios. De cada una de las muestras se analizaron y compararon los resultados generados del análisis de fragmentos de tejido tumoral obtenido tanto en fresco y conservados congelados (F/FF) como fijados en formalina e incluidos en parafina (FFPE). For this they used 108 primary MB samples. From each of the samples, the results generated from the analysis of tumor tissue fragments obtained in both fresh and frozen preservatives (F / FF) and formalin fixed and paraffin embedded (FFPE) were analyzed and compared.
El objetivo final era demostrar que el método de clasificación de MB primarios puede ser analizado mediante diversas metodologías moleculares y aplicables a diversos tipos de tejido. The ultimate goal was to demonstrate that the primary MB classification method can be analyzed by various molecular methodologies and applicable to various types of tissue.
Para la realización del método de clasificación de la invención se partió de una muestra biológica aislada de un paciente. Se procedió con la extracción de ADN mediante protocolos convencionales, tratamiento del ADN y posterior análisis de los niveles de metilación de cada una de las citosina de interés. For the realization of the method of classification of the invention, a biological sample was isolated from a patient. The DNA was extracted using conventional protocols, DNA treatment and subsequent analysis of the levels of methylation of each of the cytosine of interest.
Se utilizaron dos grupos aislados de muestras independientes de MB primarios. Two isolated groups of independent primary MB samples were used.
Grupo 1 : 96 MB congelados a -80°C (21 WNT, 26 SHH, 26 Grupo 3 y 23 Grupo 4) Group 1: 96 MB frozen at -80 ° C (21 WNT, 26 SHH, 26 Group 3 and 23 Group 4)
Grupo 2: 12 MB FFPE (2 WNT, 2 SHH, 6 Grupo 3 y 2 Grupo 4)  Group 2: 12 MB FFPE (2 WNT, 2 SHH, 6 Group 3 and 2 Group 4)
Para el control de la validez y eficiencia del procedimiento de conversión del ADN con bisulfito de sodio, se seleccionaron ocho citosinas control. Estas citosinas control mostraron un perfil de metilación muy consistente en muestras de ADN de sangre periférica normal, cuatro citosinas como control positivo metilado y cuatro como control negativo no metilado (Tabla 1 1). Tabla 11. Citosinas de control (Sangre Periférica normal n=40). For the control of the validity and efficiency of the DNA conversion process with sodium bisulfite, eight control cytosines were selected. These control cytosines showed a very consistent methylation profile in normal peripheral blood DNA samples, four cytosines as a positive methylated control and four as a non-methylated negative control (Table 1 1). Table 11. Control cytosines (Normal Peripheral Blood n = 40).
Figure imgf000046_0001
Figure imgf000046_0001
*Promedio de valor de metilación en ADN de muestra de sangre periférica normal Como primer paso se procedió con la extracción del ADN de las muestras congeladas en fresco utilizando el kit Gentra Puregene Tissue (Qiagen Technologies) o similar, siguiendo las instrucciones del fabricante. La cuantificación del ADN se realizó por lectura de la absorbancia a 260nm de longitud de onda, en un espectrofotometro (Nanodrop N-1000, Thermo Scientific) o similar. La pureza del ADN se evaluó mediante la absorbancia 260nm y el coeficiente de absorbancia a 260/280 nm, considerándose los valores óptimos entre 1 ,6 -1 ,9 unidades de densidad óptica (D.O.).  * Average methylation value in DNA of normal peripheral blood sample As a first step, DNA was extracted from fresh frozen samples using the Gentra Puregene Tissue kit (Qiagen Technologies) or similar, following the manufacturer's instructions. DNA quantification was performed by absorbance reading at 260nm wavelength, in a spectrophotometer (Nanodrop N-1000, Thermo Scientific) or similar. The DNA purity was evaluated by 260nm absorbance and the absorbance coefficient at 260/280 nm, considering the optimal values between 1, 6 -1, 9 units of optical density (D.O.).
En aquellos tumores fijados en formalina e incluidos en parafina (FFPE) procedió con la extracción del ADN de las muestras utilizando el kit QlAamp DNA FFPE (Qiagen Technologies) o similar, siguiendo las instrucciones del fabricante. In those tumors fixed in formalin and included in paraffin (FFPE) proceeded with the extraction of DNA from the samples using the QlAamp DNA FFPE kit (Qiagen Technologies) or similar, following the manufacturer's instructions.
El paso inicial de las técnicas moleculares utilizadas para analizar el estado de metilacion es la conversión del ADN con bisulfito de sodio (NaHSOs). Para ello se partió de 1 ng - 2μg de ADN y se procedió con la conversión del ADN utilizando el kit EpiTect Plus Bisulfite Conversión (Qiagen Technologies) o similar, siguiendo las instrucciones del proveedor (Tabla 12 y Tabla 13). Véase además descripción detallada de la metodología en el artículo del autor Darst RP et al. Bisulfite Sequencing of DNA. Current Protocols of Molecular Biology (2010) doi:10. 1002/0471142727. mb0709s91. The initial step of the molecular techniques used to analyze the state of methylation is the conversion of DNA with sodium bisulfite (NaHSOs). For this, the starting point was 1 ng - 2μg of DNA and the DNA was converted using the EpiTect Plus Bisulfite Conversion kit (Qiagen Technologies) or similar, following the supplier's instructions (Table 12 and Table 13). See also detailed description of the methodology in the author's article Darst RP et al. Bisulfite Sequencing of DNA. Current Protocols of Molecular Biology (2010) doi: 10. 1002/0471142727. MB0709S91.
Tabla 12. Componentes de la reacción de bisulfito. Table 12. Bisulfite reaction components.
Figure imgf000047_0001
Figure imgf000047_0001
Tabla 13. Condiciones del termociclador para conversión de bisulfito. Table 13. Thermocycler conditions for bisulfite conversion.
Paso Tiempo Temperatura  Step Time Temperature
Desnaturalización 5 minutos 95°C  Denaturation 5 minutes 95 ° C
Incubación 25 minutos 60°C Desnaturalización 5 minutos 95°C Incubation 25 minutes 60 ° C Denaturation 5 minutes 95 ° C
Incubación 85 minutos (1 h 25 60°C  Incubation 85 minutes (1 h 25 60 ° C
minutos)  minutes)
Desnaturalización 5 minutos 95°C  Denaturation 5 minutes 95 ° C
Incubación 175 minutos (2 h 55 60°C  Incubation 175 minutes (2 h 55 60 ° C
minutos)  minutes)
Modo de espera Indefinido 20°C  Standby mode Undefined 20 ° C
Amplificación selectiva de fragmentos de ADN de interés mediante reacción en cadena de la polimerasa (PCR). Selective amplification of DNA fragments of interest by polymerase chain reaction (PCR).
Como primer paso se procedió con el diseño bioinformático de cebadores específicos para alelos metilados y alelos no metilados (Tablas 14, 15 y 16). Para el diseño de los cebadores se utilizó la secuencia de GenBank (https:/A*vww.ncbi.nlm.nih.gov/genbank/) correspondiente a la localización de cada una de las citosinas de interés. Los cebadores fueron diseñados manualmente utilizando las secuencias modificadas en todas las citosinas y posteriormente analizados mediante los programas para diseño y análisis de cebadores tales como: Methyl Primer Express Software (Thermo Fisher Scientific), MethPrimer (http://www.urogene.org/methprimer2/), BiSearch (http://bisearch.enzim.hu/), entre otros.  As a first step, we proceeded with the bioinformatic design of specific primers for methylated and nonmethylated alleles (Tables 14, 15 and 16). The GenBank sequence (https: /A*vww.ncbi.nlm.nih.gov/genbank/) corresponding to the location of each of the cytosines of interest was used for the design of the primers. The primers were designed manually using the modified sequences in all cytosines and subsequently analyzed using programs for primer design and analysis such as: Methyl Primer Express Software (Thermo Fisher Scientific), MethPrimer (http://www.urogene.org/ methprimer2 /), BiSearch (http://bisearch.enzim.hu/), among others.
Tabla 14. Tabla de cebadores de BSP del Panel WNT-SHH Table 14. Table of BSP primers of the WNT-SHH Panel
ID Ta ID T a
ID lllumina Cebador Producto PCR  Illumine ID Primer PCR Product
Citosina anillamiento  Cytosine banding
WNT1_M cg2554204 (SEQ ID NO 1)  WNT1_M cg2554204 (SEQ ID NO 1)
261 58  261 58
B 1 (SEQ ID N0 2)  B 1 (SEQ ID N0 2)
WNT2_M cg2428064 (SEQ ID NO 3)  WNT2_M cg2428064 (SEQ ID NO 3)
254 60  254 60
B 5 (SEQ ID NO 4)  B 5 (SEQ ID NO 4)
WNT3_M cg0222703 (SEQ ID NO 5)  WNT3_M cg0222703 (SEQ ID NO 5)
196 58  196 58
B 6 (SEQ ID NO 6)  B 6 (SEQ ID NO 6)
N- cg 1884958 (SEQ ID NO 7)  N- cg 1884958 (SEQ ID NO 7)
161 55  161 55
WS1_MB 3 (SEQ ID NO 8)  WS1_MB 3 (SEQ ID NO 8)
N- cg 1982886 (SEQ ID NO 9) 196 58 WS2_MB 9 (SEQ ID NO 10) N- cg 1982886 (SEQ ID NO 9) 196 58 WS2_MB 9 (SEQ ID NO 10)
N- cg0126834 (SEQ ID NO 11)  N- cg0126834 (SEQ ID NO 11)
220 58 220 58
WS3_MB 5 (SEQ ID NO 12) WS3_MB 5 (SEQ ID NO 12)
SHH1_M cg1033341 (SEQ ID NO 13)  SHH1_M cg1033341 (SEQ ID NO 13)
231 58 B 6 (SEQ ID NO 14)  231 58 B 6 (SEQ ID NO 14)
SHH2_M cg 1095944 (SEQ ID NO 15)  SHH2_M cg 1095944 (SEQ ID NO 15)
207 60 B 0 (SEQ ID NO 16)  207 60 B 0 (SEQ ID NO 16)
SHH3_M cg 1292535 (SEQ ID NO 17)  SHH3_M cg 1292535 (SEQ ID NO 17)
162 58 B 5 (SEQ ID NO 18)  162 58 B 5 (SEQ ID NO 18)
Tabla 15. Tabla de cebadores de BSP del Panel G3-G4 Table 15. Table of BSP primers of Panel G3-G4
ID Ta ID T a
ID lllumina Cebador Producto PCR Illumine ID Primer PCR Product
Citosina anillamientoCytosine banding
Gr3- cg 1354894 (SEQ ID NO 19) Gr3- cg 1354894 (SEQ ID NO 19)
220 58 A_MB 6 (SEQ ID NO 20)  220 58 A_MB 6 (SEQ ID NO 20)
Gr3- cg0567960 (SEQ ID NO 21)  Gr3- cg0567960 (SEQ ID NO 21)
189 58 B_MB 9 (SEQ ID NO 22)  189 58 B_MB 9 (SEQ ID NO 22)
Gr3- cg0992923 (SEQ ID NO 23)  Gr3- cg0992923 (SEQ ID NO 23)
365 58 C_MB 8 (SEQ ID NO 24)  365 58 C_MB 8 (SEQ ID NO 24)
Gr3- cg2404447 (SEQ ID NO 25)  Gr3- cg2404447 (SEQ ID NO 25)
206 60 D_MB 8 (SEQ ID NO 26)  206 60 D_MB 8 (SEQ ID NO 26)
Gr4- cg0812933 (SEQ ID NO 27)  Gr4- cg0812933 (SEQ ID NO 27)
181 60 A_MB 1 (SEQ ID NO 28)  181 60 A_MB 1 (SEQ ID NO 28)
Gr4- cg 1040065 (SEQ ID NO 29)  Gr4- cg 1040065 (SEQ ID NO 29)
230 60 B_MB 2 (SEQ ID NO 30)  230 60 B_MB 2 (SEQ ID NO 30)
Gr4- cg 1256558 (SEQ ID NO 31)  Gr4- cg 1256558 (SEQ ID NO 31)
232 58 C_MB 5 (SEQ ID NO 32)  232 58 C_MB 5 (SEQ ID NO 32)
Gr4- cg1616705 (SEQ ID NO 33)  Gr4- cg1616705 (SEQ ID NO 33)
256 60 D_MB 2 (SEQ ID NO 34) Tabla 16. Cebadores control para BSP 256 60 D_MB 2 (SEQ ID NO 34) Table 16. Control primers for BSP
Figure imgf000050_0001
Figure imgf000050_0001
Sucesivamente se procedió con la preparación de la mezcla de reactivos de PCR (Tabla 17). Tabla 17. Mezcla reactivos para PCR. Subsequently, the PCR reagent mixture was prepared (Table 17). Table 17. Mixture reagents for PCR.
Figure imgf000051_0001
Figure imgf000051_0001
En tubos de PCR de 0.2 mi se dispensaron 24μΙ de mezcla de reactivos (Tabla 17) y 1 μΙ del ADN convertido con bisulfito correspondiente (a 50ng^l). Cada reacción contaba con su control negativo en el cual en vez de muestra de ADN se añadió agua estéril. In 0.2 ml PCR tubes 24μΙ of reagent mixture (Table 17) and 1 μΙ of the corresponding bisulfite converted DNA (at 50ng ^ l) were dispensed. Each reaction had its negative control in which sterile water was added instead of a DNA sample.
Se aplicaron las siguientes condiciones del termociclador: desnaturalización inicial a 95°C, 5 minutos (35 ciclos); desnaturalización a 95°C, 15 segundos, anillamiento a temperatura adecuada (temperaturas de anillado varían según el fragmento a analizar, Tablas 14, 15 y 16), 15 segundos, extensión a 72°C, 30 segundos, extensión final a 72°C, 7 minutos. Al final se programó la máquina para que mantuviera los tubos a 4°C (modo de espera). Finalmente, se procedió con la electroforesis de las muestras en gel de agarosa al 2%. Los tiempos y número de ciclos de la reacción de PCR pueden variar según optimización. The following conditions of the thermal cycler were applied: initial denaturation at 95 ° C, 5 minutes (35 cycles); denaturation at 95 ° C, 15 seconds, banding at appropriate temperature (banding temperatures vary according to the fragment to be analyzed, Tables 14, 15 and 16), 15 seconds, extension at 72 ° C, 30 seconds, final extension at 72 ° C , 7 minutes. In the end the machine was programmed to keep the tubes at 4 ° C (standby mode). Finally, the samples were electrophoresed with 2% agarose gel. The times and number of cycles of the PCR reaction may vary according to optimization.
Secuenciación de ADN convertido con bisulfito Sequencing of bisulfite converted DNA
A partir del producto amplificado de ADN convertido con bisulfito se procedió con el análisis de los patrones de metilación de las citosinas de interés (Panel WNT- SHH y Panel G3-G4) mediante secuenciación automatizada específica de ADN convertido por bisulfito (BSP).  From the amplified product of bisulfite converted DNA, the cytosine methylation patterns of interest (Panel WNT-SHH and Panel G3-G4) were analyzed by means of specific automated sequencing of bisulfite-converted DNA (BSP).
Como primer paso se procedió con la purificación del producto amplificado por PCR mediante el kit ExoSAP-IT® (USB-Affymetrix) o similar, siguiendo las instrucciones del proveedor. A continuación, se añadió a cada tubo 2.5μΙ de producto de PCR y 1 μΙ ExoSAP-IT®. Se colocaron los tubos en un termociclador a 37°C, 15 minutos (1 ciclo) y posteriormente, 80°C, 15 minutos (1 ciclo). Finalmente, se añadieron 22μΙ de agua al producto purificado. As a first step we proceeded with the purification of the amplified product by PCR using the ExoSAP-IT® kit (USB-Affymetrix) or similar, following the supplier's instructions. Next, 2.5μΙ of PCR product and 1μΙ ExoSAP-IT® was added to each tube. The tubes were placed in a thermocycler at 37 ° C, 15 minutes (1 cycle) and then, 80 ° C, 15 minutes (1 cycle). Finally, 22μΙ of water was added to the purified product.
A continuación se realizó la reacción de secuencia utilizando los mismos cebadores utilizados para la reacción de amplificación por PCR (Tabla 18). En la mezcla de reactivos se utilizaron por separado los cebadores Forward (Fw) y Reverse (Rv) para alelos metilados y alelos no metilados. Por cada secuencia se añadió 1 μΙ de producto de PCR purificado mediante ExoSAP-IT® los siguientes reactivos: The sequence reaction was then performed using the same primers used for the PCR amplification reaction (Table 18). In the reagent mixture, the Forward (Fw) and Reverse (Rv) primers were used separately for methylated and nonmethylated alleles. For each sequence 1 μΙ of PCR product purified by ExoSAP-IT® was added the following reagents:
Tabla 18. Componentes de la reacción de secuenciacion. Table 18. Components of the sequencing reaction.
Figure imgf000052_0001
Figure imgf000052_0001
* Big Dye Terminator v3. 1 Cycle Sequencing Kit (Applied Biosystems) o similar. * Big Dye Terminator v3. 1 Cycle Sequencing Kit (Applied Biosystems) or similar.
Se colocaron los tubos en un termociclador y se procedió según las siguientes condiciones de temperatura y ciclos: desnaturalización inicial del ADN templado a 96°C, 1 minuto. A continuación, 25 ciclos de desnaturalización a 96°C, 10 segundos, anillamiento a 50°C, 5 segundos, extensión a 60°C, 4 minutos. The tubes were placed in a thermocycler and proceeded according to the following temperature and cycle conditions: initial denaturation of the tempered DNA at 96 ° C, 1 minute. Then, 25 cycles of denaturation at 96 ° C, 10 seconds, banding at 50 ° C, 5 seconds, extension at 60 ° C, 4 minutes.
Posteriormente, se procedió con la precipitación del producto de la reacción de secuencia mediante Sephadex G-50® (GE Healthcare Life Science) o similar, siguiendo las instrucciones del proveedor. De forma resumida, se añadieron 10 μΙ del producto de secuenciacion a la columna AutoSeqTM G-50© del kit (GESubsequently, the sequence reaction product was precipitated by Sephadex G-50® (GE Healthcare Life Science) or the like, following the instructions of the supplier. In summary, 10 μΙ of the sequencing product was added to the AutoSeqTM G-50 © column of the kit (GE
Healthcare Life Science) o similar, previamente preparada con la solución Sephadex G-50© y se procedió a centrifugar 2 minutos a 4,500 rpm a temperatura ambiente. Se transfirió el eluído a una placa de secuenciacion. Finalmente, se analizaron las muestras mediante una máquina de secuenciación automatizada. El análisis del electroferograma de la secuencia se realizó mediante el software Chromas Lite (Technelysium) o similar (Figura 6). Los autores de la invención observaron como el resultado del análisis medianteHealthcare Life Science) or similar, previously prepared with the Sephadex G-50 © solution and centrifuged 2 minutes at 4,500 rpm at room temperature. The eluate was transferred to a sequencing plate. Finally I know They analyzed the samples using an automated sequencing machine. The sequence electropherogram analysis was performed using the Chromas Lite (Technelysium) or similar software (Figure 6). The authors of the invention observed as the result of the analysis by
BSP de las citosinas de la base de datos de validación correspondientes al Panel WNT-SHH, mostraban un patrón de metilacion equivalente a las citosinas seleccionadas/originales. De forma similar, las citosinas de validación se asociaban de forma significativa y específica (concordancia 100%) con las tres entidades genéticas definidas por la OMS (2016): WNT, SHH y Grupo no-WNT/no-BSP of the validation database cytosines corresponding to the WNT-SHH Panel, showed a methylation pattern equivalent to the selected / original cytosines. Similarly, validation cytosines were significantly and specifically associated (100% concordance) with the three genetic entities defined by WHO (2016): WNT, SHH and Non-WNT / no- Group
SHH de MB. Se obtuvieron los mismos resultados partiendo tanto de ADN extraído de tejido tumoral en F/FF como de tejido FFPE (Figura 6). MB SHH. The same results were obtained from both DNA extracted from tumor tissue in F / FF and from FFPE tissue (Figure 6).
El análisis mediante la metodología BSP de las citosinas correspondientes al Panel G3-G4, mostró una especificidad moderada del patrón de metilacion y capacidad de discriminar los tumores del Grupo 3 y el Grupo 4. Debido al patrón de metilacion no claramente bimodal de las citosinas del Panel G3-G4, se observó la presencia de dobles picos en el electroferograma de algunas de las secuencias BSP. En algún caso, esto dificultó la interpretación del resultado. Los autores de la invención constataron como la metodología BSP no era la más adecuada para el análisis del perfil de metilacion del Grupo 3 y el Grupo 4. The analysis using the BSP methodology of the cytosines corresponding to Panel G3-G4, showed a moderate specificity of the methylation pattern and the ability to discriminate tumors of Group 3 and Group 4. Due to the non-clearly bimodal methylation pattern of cytosines of Panel G3-G4, the presence of double peaks in the electropherogram of some of the BSP sequences was observed. In some cases, this made it difficult to interpret the result. The authors of the invention found that the BSP methodology was not the most suitable for the analysis of the methylation profile of Group 3 and Group 4.
Pirosecuenciación por bisulfito Pyrosesequencing by bisulfite
A partir del ADN convertido con bisulfito sódico, también se procedió con la cuantificación de los niveles de metilacion de las citosinas de interés (Panel WNT- From the DNA converted with sodium bisulfite, the methylation levels of the cytosines of interest were also quantified (Panel WNT-
SHH y Panel G3-G4) mediante el método de pirosecuenciación de bisulfito. Para ello se emplearon muestras de ADN extraído tanto de tejido tumoral en F/FF como en FFPE. Para poder analizar las citosinas de interés se diseñaron parejas de cebadores biotinilados (en 5'-terminal), específicos para alelos metilados y alelos no metilados (Tabla 19, 20 y 21). Para ello se utilizó la herramienta PyroMark Assay Design (Qiagen Technologies) o similar. Tabla 19. Cebadores de pirosecuenciacion para el Panel WNT-SHH SHH and Panel G3-G4) by the bisulfite pyrosequencing method. For this, DNA samples extracted from both tumor tissue in F / FF and in FFPE were used. In order to analyze the cytosines of interest, pairs of biotinylated (5'-terminal) primers, specific for methylated and nonmethylated alleles (Table 19, 20 and 21), were designed. For this, the PyroMark Assay Design (Qiagen Technologies) or similar tool was used. Table 19. Pyrosequencing primers for the WNT-SHH Panel
Tipo de Cebadores/Sondas Type of Primers / Probes
D Citosina ID lllumina D Cytosine ID lllumina
Cebador 5' - 3'  Primer 5 '- 3'
Cebador (SEQ ID NO 1) Primer (SEQ ID NO 1)
Cebador + Primer +
WNT1_MB cg25542041 (SEQ ID NO 2)  WNT1_MB cg25542041 (SEQ ID NO 2)
Biotina  Biotin
Sonda (SEQ ID NO 39) Probe (SEQ ID NO 39)
Cebador + Primer +
(SEQ ID NO 3) Biotina  (SEQ ID NO 3) Biotin
WNT2_MB cg24280645  WNT2_MB cg24280645
Cebador (SEQ ID NO 4) Primer (SEQ ID NO 4)
Sonda (SEQ ID NO 40)Probe (SEQ ID NO 40)
Cebador (SEQ ID NO 5)Primer (SEQ ID NO 5)
Cebador + Primer +
WNT3_MB cg02227036 (SEQ ID NO 6)  WNT3_MB cg02227036 (SEQ ID NO 6)
Biotina  Biotin
Sonda (SEQ ID NO 41) Probe (SEQ ID NO 41)
Cebador (SEQ ID NO 35)Primer (SEQ ID NO 35)
Cebador + Primer +
N-WS1_MB cg 18849583 (SEQ ID NO 36)  N-WS1_MB cg 18849583 (SEQ ID NO 36)
Biotina  Biotin
Sonda (SEQ ID NO 42) Probe (SEQ ID NO 42)
Cebador (SEQ ID NO 9)Primer (SEQ ID NO 9)
Cebador + Primer +
N-WS2_MB cg 19828869 (SEQ ID NO 10)  N-WS2_MB cg 19828869 (SEQ ID NO 10)
Biotina  Biotin
Sonda (SEQ ID NO 43) Probe (SEQ ID NO 43)
Cebador (SEQ ID NO 11)Primer (SEQ ID NO 11)
Cebador + Primer +
N-WS3_MB cg01268345 (SEQ ID NO 12)  N-WS3_MB cg01268345 (SEQ ID NO 12)
Biotina  Biotin
Sonda (SEQ ID NO 44) Probe (SEQ ID NO 44)
Cebador (SEQ ID NO 13)Primer (SEQ ID NO 13)
Cebador + Primer +
SHH1_MB cg 10333416 (SEQ ID NO 14)  SHH1_MB cg 10333416 (SEQ ID NO 14)
Biotina  Biotin
Sonda (SEQ ID NO 45) Probe (SEQ ID NO 45)
Cebador (SEQ ID NO 37)Primer (SEQ ID NO 37)
SHH2_MB cg 10959440 SHH2_MB cg 10959440
Cebador + (SEQ ID NO 38) Biotina Primer + (SEQ ID NO 38) Biotin
Sonda (SEQ ID NO 46) Probe (SEQ ID NO 46)
Cebador (SEQ ID NO 17)Primer (SEQ ID NO 17)
Cebador + Primer +
SHH3_MB cg 12925355 (SEQ ID NO 18)  SHH3_MB cg 12925355 (SEQ ID NO 18)
Biotina  Biotin
Sonda (SEQ ID NO 47)  Probe (SEQ ID NO 47)
Tabla 20. Cebadores de pirosecuenciacion para el Panel G3-G4 Table 20. Pyrosequencing primers for Panel G3-G4
Tipo de Cebadores/Sondas Type of Primers / Probes
ID Citosina ID lllumina ID Cytosine ID lllumina
Cebador 5' - 3'  Primer 5 '- 3'
Cebador (SEQ ID NO 19) Primer (SEQ ID NO 19)
Cebador + Primer +
Gr3-A_MB cg 13548946 (SEQ ID NO 20)  Gr3-A_MB cg 13548946 (SEQ ID NO 20)
Biotina  Biotin
Sonda (SEQ ID NO 72) Probe (SEQ ID NO 72)
Cebador (SEQ ID NO 21)Primer (SEQ ID NO 21)
Cebador + Primer +
Gr3-B_MB cg05679609 (SEQ ID NO 22)  Gr3-B_MB cg05679609 (SEQ ID NO 22)
Biotina  Biotin
Sonda (SEQ ID NO 73) Probe (SEQ ID NO 73)
Cebador (SEQ ID NO 23)Primer (SEQ ID NO 23)
Cebador + Primer +
Gr3-C_MB cg09929238 (SEQ ID NO 24)  Gr3-C_MB cg09929238 (SEQ ID NO 24)
Biotina  Biotin
Sonda (SEQ ID NO 74) Probe (SEQ ID NO 74)
Cebador (SEQ ID NO 25)Primer (SEQ ID NO 25)
Cebador + Primer +
Gr3-D_MB cg24044478 (SEQ ID NO 26)  Gr3-D_MB cg24044478 (SEQ ID NO 26)
Biotina  Biotin
Sonda (SEQ ID NO 75) Probe (SEQ ID NO 75)
Cebador (SEQ ID NO 27)Primer (SEQ ID NO 27)
Cebador + Primer +
Gr4-A_MB cg08129331 (SEQ ID NO 28)  Gr4-A_MB cg08129331 (SEQ ID NO 28)
Biotina  Biotin
Sonda (SEQ ID NO 76) Probe (SEQ ID NO 76)
Cebador (SEQ ID NO 29)Primer (SEQ ID NO 29)
Gr4-B_MB cg 10400652 Cebador + Gr4-B_MB cg 10400652 Primer +
(SEQ ID NO 30) Biotina Sonda (SEQ ID NO 77) (SEQ ID NO 30) Biotin Probe (SEQ ID NO 77)
Cebador (SEQ ID NO 31) Primer (SEQ ID NO 31)
Cebador + Primer +
Gr4-C_MB cg 12565585 (SEQ ID NO 32)  Gr4-C_MB cg 12565585 (SEQ ID NO 32)
Biotina  Biotin
Sonda (SEQ ID NO 78) Probe (SEQ ID NO 78)
Cebador (SEQ ID NO 33)Primer (SEQ ID NO 33)
Cebador + Primer +
Gr4-D_MB cg 16167052 (SEQ ID NO 34)  Gr4-D_MB cg 16167052 (SEQ ID NO 34)
Biotina  Biotin
Sonda (SEQ ID NO 79)  Probe (SEQ ID NO 79)
Tabla 21. Cebadores control para pirosecuenciacion Table 21. Control primers for pyrosequencing
Tipo de Kind of
ID citosina ID ¡Ilumina Cebadores 5'-3'  ID cytosine ID Illuminates Primers 5'-3 '
cebador  primer
Cebador (SEQ ID NO 48) Primer (SEQ ID NO 48)
Cebador + Primer +
CP1_MB cg 13458561 (SEQ ID NO 49)  CP1_MB cg 13458561 (SEQ ID NO 49)
Biotina  Biotin
Sonda (SEQ ID NO 50) Probe (SEQ ID NO 50)
Cebador (SEQ ID NO 51)Primer (SEQ ID NO 51)
Cebador + Primer +
CP2_MB cg 19602374 (SEQ ID NO 52)  CP2_MB cg 19602374 (SEQ ID NO 52)
Biotina  Biotin
Sonda (SEQ ID NO 53) Probe (SEQ ID NO 53)
Cebador (SEQ ID NO 54)Primer (SEQ ID NO 54)
Cebador + Primer +
CP3_MB cgO 1724941 (SEQ ID NO 55)  CP3_MB cgO 1724941 (SEQ ID NO 55)
Biotina  Biotin
Sonda (SEQ ID NO 56) Probe (SEQ ID NO 56)
Cebador (SEQ ID NO 57)Primer (SEQ ID NO 57)
Cebador + Primer +
CP4_MB cg 12203543 (SEQ ID NO 58)  CP4_MB cg 12203543 (SEQ ID NO 58)
Biotina  Biotin
Sonda (SEQ ID NO 59) Probe (SEQ ID NO 59)
Cebador (SEQ ID NO 60)Primer (SEQ ID NO 60)
CN1_MB cg22885965 Cebador + CN1_MB cg22885965 Primer +
(SEQ ID NO 61) Biotina Sonda (SEQ ID NO 62) (SEQ ID NO 61) Biotin Probe (SEQ ID NO 62)
Cebador (SEQ ID NO 63)  Primer (SEQ ID NO 63)
Cebador +  Primer +
CN2_MB cg05854826 (SEQ ID NO 64)  CN2_MB cg05854826 (SEQ ID NO 64)
Biotina  Biotin
Sonda (SEQ ID NO 65)  Probe (SEQ ID NO 65)
Cebador (SEQ ID NO 66)  Primer (SEQ ID NO 66)
Cebador +  Primer +
CN3_MB cg05584166 (SEQ ID NO 67)  CN3_MB cg05584166 (SEQ ID NO 67)
Biotina  Biotin
Sonda (SEQ ID NO 68)  Probe (SEQ ID NO 68)
Cebador (SEQ ID NO 69)  Primer (SEQ ID NO 69)
Cebador +  Primer +
CN4_MB cg06319390 (SEQ ID NO 70)  CN4_MB cg06319390 (SEQ ID NO 70)
Biotina  Biotin
Sonda (SEQ ID NO 71)  Probe (SEQ ID NO 71)
Se partió primero con la amplificación de los fragmentos de ADN de interés que se llevó a cabo con el kit PyroMark® PCR (Qiagen Techologies) o similar, siguiendo las instrucciones del proveedor (Tabla 19, 20 y 21). A continuación, se mezclaron los reactivos necesarios para la amplificación por PCR según los volúmenes y concentraciones descritas en la Tabla 22. It was first started with the amplification of the DNA fragments of interest that was carried out with the PyroMark® PCR kit (Qiagen Techologies) or similar, following the supplier's instructions (Table 19, 20 and 21). Next, the reagents necessary for PCR amplification were mixed according to the volumes and concentrations described in Table 22.
Tabla 22. Composición de la mezcla de reactivos para la amplificación de una región de ADN convertida con bisulfito para pirosecuenciación. Table 22. Composition of the reagent mixture for the amplification of a bisulfite converted DNA region for pyrosequencing.
Componente Volumen por Concentración  Volume by Concentration Component
reacción final  final reaction
PyroMark PCR Master Mix, 2x 12.5 μΙ 1x  PyroMark PCR Master Mix, 2x 12.5 μΙ 1x
CoralLoad Concéntrate, 10x 2.5 1x  CoralLoad Concentrate, 10x 2.5 1x
25 mM MgCI2 (opcional) Variable ≥1.5 mM  25 mM MgCI2 (optional) Variable ≥1.5 mM
Solución-Q, 5x (opcional) 5 μΙ 1x  Solution-Q, 5x (optional) 5 μΙ 1x
Cebador A/Cebador B Variable/variab 0.2 μΜ/0.2 μΜ  Primer A / Primer B Variable / variab 0.2 μΜ / 0.2 μΜ
le  you
Agua libre de ARN-asa Variable - Water free of RNA-handle Variable -
Volumen total (tras la adición del 25 Total volume (after the addition of 25
molde de ADN) Se introdujeron los tubos de PCR en el termociclador y se procedió según las condiciones descritas en la Tabla 23, protocolo estándar sujeto a optimización. DNA mold) The PCR tubes were introduced into the thermal cycler and proceeded according to the conditions described in Table 23, standard protocol subject to optimization.
Tabla 23. Protocolo estándar para PyroMark PCR Master Mix Table 23. Standard protocol for PyroMark PCR Master Mix
Figure imgf000058_0001
Figure imgf000058_0001
Una vez terminada la amplificación, se procedió con la inmovilización de los productos de PCR con microesferas Streptavidin Sepharose High PerformanceOnce the amplification was finished, the immobilization of the PCR products with Streptavidin Sepharose High Performance microspheres was proceeded
(GE Helthcare) o similar, antes de proceder al análisis por pirosecuenciación. (GE Helthcare) or similar, before proceeding to pyrosequencing analysis.
Se preparó la muestra maestra con las microesferas "Streptavidin Sepharose High Performance" y los reactivos para la inmovilización de ADN según los datos de la Tabla 24. Se añadieron 70μΙ de mezcla maestra a cada uno de los pocilios de una placa de PCR junto a 10μΙ de PCR biotinilado (volumen total por pocilio 80μΙ) y se centrifugó la placa (1.400rpm) durante 5-10 minutos, según protocolo estándar sujeto a optimización. The master sample was prepared with the "Streptavidin Sepharose High Performance" microspheres and DNA immobilization reagents according to the data in Table 24. 70μΙ of master mix was added to each well of a PCR plate next to 10μΙ of biotinylated PCR (total volume per well 80μΙ) and the plate was centrifuged (1,400rpm) for 5-10 minutes, according to standard protocol subject to optimization.
Tabla 24. Streptavidin Sepharose High Performance PCR Master Mix Componente Volumen/Muestra (μ ) Table 24. Streptavidin Sepharose High Performance PCR Master Mix Volume / Sample Component (μ)
S!repttivi in Sspho!ose High Performance 2  S! Repttivi in Sspho! Ose High Performance 2
Agua Η-,Ο^ suministra a) 28 Water Η-, Ο ^ supplies a) 28
A continuación, se procedió con la preparación de las muestras previa al análisis por pirosecuenciación en el PyroMark Q24 (Qiagen) o similar. En la placa PyroMark Q24 (Qiagen) o similar, se añadieron en cada pocilio 40μΙ de tampón de aliniamento y 0,5μΙ de cebador especifico para la los productos de PCR. Se posicionó la placa PyroMark Q96 Píate Low en el lugar correspondiente en la estación de vacío (Qiagen) o similar. De la misma forma se colocó la placa de PCR en la correspondiente posición en la estación de vacío. Se introdujeron las sondas de vacío en la placa de PCR para capturar las microesferas con los productos de PCR inmovilizados. Tras una serie de lavados, se liberaron las microesferas en la placa PyroMark Q24, siguiendo las recomendaciones del fabricante (Manual del usuario PyroMark Q24, Qiagen). Finalmente, se calentó la placa a 85°C, 2 minutos. Then, the samples were prepared prior to the pyrosequencing analysis in PyroMark Q24 (Qiagen) or similar. In the PyroMark Q24 plate (Qiagen) or similar, 40μΙ of alliniament buffer and 0.5μΙ of specific primer for the PCR products were added in each well. The PyroMark Q96 Píate Low plate was positioned in the corresponding place in the vacuum station (Qiagen) or similar. In the same way the PCR plate was placed in the corresponding position in the vacuum station. Vacuum probes were introduced into the PCR plate to capture the microspheres with immobilized PCR products. After a series of washes, the microspheres were released on the PyroMark Q24 plate, following the manufacturer's recommendations (PyroMark Q24 User Manual, Qiagen). Finally, the plate was heated to 85 ° C, 2 minutes.
A continuación se procedió con la carga de los reactivos en el cartucho PyroMark Q24 (Qiagen) o similar, y posicionamiento de dicho cartucho en el sistema PyroMark Q24. Los reactivos incluyen una mezcla de enzimas, mezcla de substratos y nucleótidos (A, T, G, C), según las recomendaciones del fabricanteNext, the reagents were loaded into the PyroMark Q24 cartridge (Qiagen) or similar, and positioning of said reagent in the PyroMark Q24 system. Reagents include a mixture of enzymes, a mixture of substrates and nucleotides (A, T, G, C), according to the manufacturer's recommendations
(Manual del usuario PyroMark Q24, Qiagen). (PyroMark Q24 user manual, Qiagen).
Asimismo, se introdujo la placa en el bloque térmico del sistema PyoMark Q24 y se procedió a ejecutar el ensayo de pirosecuenciación. Al finalizar el ensayo, se procedió a documentar e interpretar los resultados de la cuantificación de la metilación en el pirograma/histograma obtenido (Tablas 25, 26 y Figura 7). Likewise, the plate was introduced in the thermal block of the PyoMark Q24 system and the pyrosequencing test was carried out. At the end of the trial, the results of methylation quantification were documented and interpreted in the pyrogram / histogram obtained (Tables 25, 26 and Figure 7).
Los resultados del análisis mediante pirosecuenciación de las citosinas de la base de datos de validación correspondientes al Panel WNT-SHH mostraban un patrón de metilación equivalente a las citosinas de la cohorte de estudio (Figuras 3A y 3B). De forma similar, las citosinas de validación se asociaban de forma significativa y específica con las tres entidades genéticas definidas por la OMS (2016): WNT, SHH y Grupo no-WNT/no-SHH de MB. Asimismo los resultados del Panel G3-G4 mostraron un patrón de metilación específico, similar a las citosinas de la cohorte de estudio (Figuras 4A y 4B). La aplicación de la función LDA a los valores de pirosecuenciacion del Panel WNT-SHH permitió clasificar la totalidad de las muestras con una concordancia del 100% (Tablas 25 y 26). The results of the pyrosequencing analysis of the cytosines of the validation database corresponding to the WNT-SHH Panel showed a methylation pattern equivalent to the cytosines of the study cohort (Figures 3A and 3B). Similarly, validation cytosines were significantly and specifically associated with the three genetic entities defined by WHO (2016): WNT, SHH and MB-non-WNT / non-SHH Group. Likewise, the results of Panel G3-G4 showed a specific methylation pattern, similar to the cytosines of the study cohort (Figures 4A and 4B). The application of the LDA function to the pyrosequencing values of the WNT-SHH Panel allowed to classify all the samples with a concordance of 100% (Tables 25 and 26).
Tabla 25. Ejemplo de la capacidad de clasificación del Panel WNT-SHH en tejido tumoral en F/FF. Table 25. Example of the classification capacity of the WNT-SHH Panel in tumor tissue in F / FF.
Muestras no-WNT/no-SHH SHH WNT Predicción Panel Non-WNT / non-SHH SHH WNT Prediction Panel Samples
WNT-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHH no-WNT/no-SHH 100% 0% 0% no-WNT/no-SHHWNT-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0 % 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0 % 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0 % 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0 % 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0 % 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH no-WNT / no-SHH 100% 0% 0% no-WNT / no-SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHH SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH SHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
SHH 0% 100% 0% SHHSHH 0% 100% 0% SHH
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNTWNT 0% 0% 100% WNT
WNT 0% 0% 100% WNT WNT 0% 0% 100% WNT WNT 0% 0% 100% WNT WNT 0% 0% 100% WNT
WNT 0% 0% 100% WNT  WNT 0% 0% 100% WNT
Tabla 26. Ejemplo de la capacidad de clasificación del Panel WNT-SHH en tejido tumoral en FFPE. Table 26. Example of the classification capacity of the WNT-SHH Panel in tumor tissue in FFPE.
Figure imgf000063_0001
Figure imgf000063_0001
En las Tablas 25 y 26, la Columna izquierda representa la clasificación molecular según datos publicados ((Northcott PA et al. Medulloblastoma Comprises Four Distinct Molecular Variants. Journal of Clinical Oncology 2011, 29(11): 1408-1414)). En el centro, la afiliación según los resultados de pirosecuenciación y en la derecha la clasificación según el panel WNT-SHH. In Tables 25 and 26, the left column represents the molecular classification according to published data ((Northcott PA et al. Medulloblastoma Comprises Four Distinct Molecular Variants. Journal of Clinical Oncology 2011, 29 (11): 1408-1414)). In the center, the affiliation according to the pyrosequencing results and in the right the classification according to the WNT-SHH panel.
Aplicaron el mismo procedimiento a los valores del Panel G3-G4 a las muestras clasificadas como grupo no-WNT/no-SHH por el Panel WNT-SHH. Se clasificaron correctamente 47 de 49 de las muestras analizadas de ADN extraído de tejido en FF y 6 de las 8 muestras de FFPE They applied the same procedure to the values of Panel G3-G4 to samples classified as a non-WNT / non-SHH group by the WNT-SHH Panel. 47 of 49 of the analyzed samples of DNA extracted from tissue in FF were correctly classified and 6 of the 8 samples of FFPE
En las siguientes tablas se muestra el resumen de resultados del análisis de los niveles de metilación del panel G3-G4 mediante pirosecuenciación por bisulfito en ADN de tejido F/FF y FFPE de meduloblastoma (Tablas 27 y 28). Tabla 27. Ejemplo de la capacidad de clasificación del Panel G3-G4 en tejido tumoral en F/FF The following tables show the summary of results of the analysis of the levels of methylation of the G3-G4 panel by pyrosequencing by bisulfite in DNA of F / FF tissue and FFPE of medulloblastoma (Tables 27 and 28). Table 27. Example of the classification capacity of Panel G3-G4 in tumor tissue in F / FF
Muestras Grupo 3 Grupo 4 Predicción Panel G3-G4 Samples Group 3 Group 4 Prediction Panel G3-G4
Grupo 3 100% 0% Grupo 3 Group 3 100% 0% Group 3
Grupo 4 97% 3% Grupo 3  Group 4 97% 3% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 100% 0% Grupo 3  Group 3 100% 0% Group 3
Grupo 3 0% 100% Grupo 4  Group 3 0% 100% Group 4
Grupo 3 0% 100% Grupo 4  Group 3 0% 100% Group 4
Grupo 4 25% 75% Grupo 4  Group 4 25% 75% Group 4
Grupo 4 4% 96% Grupo 4 Grupo 4 0% 100% Grupo 4 Group 4 4% 96% Group 4 Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Grupo 4 0% 100% Grupo 4  Group 4 0% 100% Group 4
Tabla 28. Ejemplo de la capacidad de clasificación del Panel G3-G4 en tejido tumoral en FFPE. Table 28. Example of the classification capacity of Panel G3-G4 in tumor tissue in FFPE.
Muestras Grupo 3 Grupo 4 Predicción panel  Samples Group 3 Group 4 Prediction panel
G3-G4  G3-G4
Grupo 3 FFPE 100% 0% Grupo 3 FFPE  Group 3 FFPE 100% 0% Group 3 FFPE
Grupo 3 FFPE 100% 0% Grupo 3 FFPE  Group 3 FFPE 100% 0% Group 3 FFPE
Grupo 3 FFPE 100% 0% Grupo 3 FFPE  Group 3 FFPE 100% 0% Group 3 FFPE
Grupo 3 FFPE 100% 0% Grupo 3 FFPE  Group 3 FFPE 100% 0% Group 3 FFPE
Grupo 3 FFPE 0% 100% Grupo 4 FFPE  Group 3 FFPE 0% 100% Group 4 FFPE
Grupo 3 FFPE 0% 100% Grupo 4 FFPE  Group 3 FFPE 0% 100% Group 4 FFPE
Grupo 4 FFPE 0% 100% Grupo 4 FFPE Grupo 4 FFPE 0% 100% Grupo 4 FFPE Group 4 FFPE 0% 100% Group 4 FFPE Group 4 FFPE 0% 100% Group 4 FFPE
En la columna a la izquierda de las Tablas 27 y 28 se puede ver la clasificación molecular según datos publicados (Northcott PA et al. Medulloblastoma Comprises Four Distinct Molecular Variants. Journal of Clinical Oncology 2011, 29(11): 1408- 1414). En las columnas en el centro se muestra la afiliación según los resultados de la pirosecuenciación y en la columna de la derecha, la clasificación según el Panel G3-G4. The molecular classification according to published data can be seen in the column to the left of Tables 27 and 28 (Northcott PA et al. Medulloblastoma Comprises Four Distinct Molecular Variants. Journal of Clinical Oncology 2011, 29 (11): 1408-1414). In the columns in the center the affiliation is shown according to the pyrosequencing results and in the right column, the classification according to Panel G3-G4.
Se obtuvieron los mismos resultados partiendo tanto de ADN extraído de tejido tumoral en F/FF como de tejido FFPE (Tablas 27 y 28). De esta forma, los resultados confirmaron la validez de los perfiles de metilación identificados mediante tecnología de microarray. The same results were obtained from both DNA extracted from tumor tissue in F / FF and from FFPE tissue (Tables 27 and 28). In this way, the results confirmed the validity of the methylation profiles identified by microarray technology.
Asimismo, se demostró que el perfil de metilación de las citosinas de interés de tejido tumoral fijado en formalina e incluido en parafina es comparable al tejido obtenido en fresco y/o conservado congelado a -80°C. Por lo tanto, al mantenerse estable el patrón de metilación de las citosinas de los marcadores propuestos, Panel WNT-SHH y Panel G3-G4, el método de clasificación propuesto es aplicable a este tipo de material biológico. Likewise, it was demonstrated that the methylation profile of the cytosines of interest of tumor tissue fixed in formalin and included in paraffin is comparable to the tissue obtained in fresh and / or preserved frozen at -80 ° C. Therefore, when the cytosine methylation pattern of the proposed markers, Panel WNT-SHH and Panel G3-G4, remains stable, the proposed classification method is applicable to this type of biological material.
Conclusiones Ejemplo 3: Conclusions Example 3:
Al utilizar diversas técnicas moleculares para el análisis del patrón de metilación del ADN, se demostró como las citosinas del Panel WNT-SHH y Panel G3-G4 son eficaces para establecer los subgrupos moleculares de las entidades genéticas WNT, SHH, Grupo 3 y Grupo 4 de MB. De esta forma, los resultados confirmaron la validez de los perfiles de metilación identificados mediante tecnología de microarray.  By using various molecular techniques for the analysis of the DNA methylation pattern, it was demonstrated how the cytosines of Panel WNT-SHH and Panel G3-G4 are effective in establishing the molecular subgroups of the genetic entities WNT, SHH, Group 3 and Group 4 of MB. In this way, the results confirmed the validity of the methylation profiles identified by microarray technology.
Asimismo, se demostró como el patrón de metilación diferencial de las citosinas que constituyen el Panel WNT-SHH y Panel G3-G4 representa un marcador eficaz para la clasificación molecular de MBs pertenecientes a las entidades genéticas WNT, SHH, Grupo 3 y Grupo 4. Finalmente, se demostró que el perfil de metilación de las citosinas de interés de tejido tumoral fijado en formalina e incluido en parafina es comparable al tejido obtenido en fresco y/o conservado congelado a -80°C. Por lo tanto, al mantenerse estable el patrón de metilación de las citosinas de los marcadores propuestos, Panel WNT-SHH y Panel G3-G4, se comprobó que el método de clasificación propuesto es aplicable a este tipo de material biológico. Likewise, it was demonstrated that the pattern of differential methylation of the cytosines that constitute Panel WNT-SHH and Panel G3-G4 represents an effective marker for the molecular classification of MBs belonging to the genetic entities WNT, SHH, Group 3 and Group 4. Finally, it was shown that the methylation profile of the cytosines of interest of tumor tissue fixed in formalin and included in paraffin is comparable to tissue obtained in fresh and / or preserved frozen at -80 ° C. Therefore, by keeping the cytosine methylation pattern of the proposed markers, Panel WNT-SHH and Panel G3-G4, stable, it was found that the proposed classification method is applicable to this type of biological material.

Claims

REIVINDICACIONES
1. Método in vitro para la clasificación de un paciente con meduloblastoma en uno de los subgrupos moleculares WNT, SHH y grupo no-WNT/no-SHH que comprende: a) Análisis de los niveles de metilacion de las citosinas WNT1_MB, WNT2_MB, WNT3_MB, N-WS1_MB, N-WS2_MB, N-WS3_MB, SHH1_MB, SHH2_MB y SHH3_MB, que forman el Panel WNT-SHH, o una combinación de las mismas, en el ADN extraído de una muestra biológica aislada del paciente, y 1. In vitro method for the classification of a patient with medulloblastoma in one of the molecular subgroups WNT, SHH and non-WNT / non-SHH group comprising: a) Analysis of cytosine methylation levels WNT1_MB, WNT2_MB, WNT3_MB , N-WS1_MB, N-WS2_MB, N-WS3_MB, SHH1_MB, SHH2_MB and SHH3_MB, which form the WNT-SHH Panel, or a combination thereof, in the DNA extracted from a biological sample isolated from the patient, and
b) Clasificación del paciente en uno de los subgrupos moleculares WNT, SHH y grupo no-WNT/no-SHH en base a los niveles de metilacion de las citosinas del Panel WNT-SHH analizadas en a), según valores de referencia (Tabla 2). b) Classification of the patient in one of the WNT, SHH and non-WNT / non-SHH molecular subgroups based on the levels of methylation of the WNT-SHH Panel cytosines analyzed in a), according to reference values (Table 2 ).
2. Método, según la reivindicación 1 , en el que, para aquellos pacientes clasificados como no-WNT/no-SHH, se llevan a cabo los siguientes pasos adicionales: c) Análisis de los niveles de metilacion de las citosinas Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB y Gr4-D_MB, que forman el Panel G3-G4, o una combinación de las mismas, en el ADN extraído de la muestra biológica aislada del paciente, y 2. Method according to claim 1, wherein, for those patients classified as non-WNT / non-SHH, the following additional steps are carried out: c) Analysis of the levels of methylation of the cytosines Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB, which form Panel G3-G4, or a combination thereof, in the DNA extracted from the sample biological isolated from the patient, and
d) Clasificación del paciente en uno de los subgrupos moleculares Grupo 3 y Grupo 4 en base a los niveles de metilacion de las citosinas del Panel G3-G4 analizadas en c), según valores de referencia (Tabla 4). d) Classification of the patient in one of the molecular subgroups Group 3 and Group 4 based on the levels of methylation of the cytosines of Panel G3-G4 analyzed in c), according to reference values (Table 4).
3. Método para la clasificación de un paciente con meduloblastoma en uno de los subgrupos moleculares WNT, SHH, Grupo 3 y Grupo 4, que comprende: 3. Method for the classification of a patient with medulloblastoma in one of the molecular subgroups WNT, SHH, Group 3 and Group 4, comprising:
A. Análisis de forma combinada de los niveles de metilacion de las citosinas del panel WNT-SHH, o una combinación de las mismas, y del Panel G3-G4, o una combinación de las mismas, en el ADN extraído de una muestra biológica aislada del paciente, y B. Clasificación del paciente en uno de los subgrupos moleculares WNT, SHH, Grupo 3 y Grupo 4, en base a los niveles de metilacion de las citosinas del Panel WNT-SHH y del Panel G3-G4 analizadas en A), según valores de referencia (Tablas 2 y 4). A. Combined analysis of the levels of methylation of the cytosines of the WNT-SHH panel, or a combination thereof, and of Panel G3-G4, or a combination thereof, in the DNA extracted from an isolated biological sample of the patient, and B. Classification of the patient in one of the molecular subgroups WNT, SHH, Group 3 and Group 4, based on the levels of cytosine methylation of Panel WNT-SHH and Panel G3-G4 analyzed in A), according to values of reference (Tables 2 and 4).
4. Método, según cualquiera de las reivindicaciones anteriores, donde la muestra biológica empleada es tejido tumoral. 4. Method according to any of the preceding claims, wherein the biological sample used is tumor tissue.
5. Método, según la reivindicación 4, donde el contenido de célula tumoral viable en la muestra de tejido tumoral es al menos del 70%. 5. A method according to claim 4, wherein the viable tumor cell content in the tumor tissue sample is at least 70%.
6. Método, según la reivindicación 4 ó 5, donde el tejido tumoral es tejido tumoral fresco, congelado ó fijado en formalina y embebido en parafina. 6. Method according to claim 4 or 5, wherein the tumor tissue is fresh, frozen or formalin fixed tumor tissue and embedded in paraffin.
7. Método, según una cualquiera de las reivindicaciones 1-6, donde el análisis de los niveles de metilacion de las citosinas se lleva a cabo mediante secuenciación específica de ADN convertido por bisulfito. 7. Method according to any one of claims 1-6, wherein the analysis of the levels of methylation of the cytosines is carried out by specific sequencing of bisulfite-converted DNA.
8. Método según una cualquiera de las reivindicaciones 1-6 donde el análisis de los niveles de metilacion de las citosinas se lleva a cabo mediante la tecnología de pirosecuenciación del ADN convertido por bisulfito. 8. A method according to any one of claims 1-6 wherein the analysis of the levels of methylation of the cytosines is carried out by the pyrosequencing technology of bisulfite-converted DNA.
9. Oligonucleótidos de secuencias SEQ ID NO 1-79 para su empleo en el análisis de los niveles de metilacion de las citosinas de los Paneles WNT-SHH y/o Gr3-G4. 9. Sequence oligonucleotides SEQ ID NO 1-79 for use in the analysis of the cytosine methylation levels of the WNT-SHH and / or Gr3-G4 Panels.
10. Perfil de metilacion de las citosinas del Panel WNT-SHH para su empleo como marcador de clasificación de pacientes con meduloblastoma en los tres subgrupos moleculares, WNT, SHH y no-WNT/no-SHH. 10. Methylation profile of the WNT-SHH Panel cytosines for use as a classification marker for patients with medulloblastoma in the three molecular subgroups, WNT, SHH and non-WNT / non-SHH.
1 1. Perfil de metilacion de las citosinas del Panel G3-G4 para su empleo como marcador de clasificación de pacientes con meduloblastoma en los subgrupos moleculares, Grupo 3 y Grupo 4. 1 1. Methylation profile of the cytosines of Panel G3-G4 for use as a classification marker for patients with medulloblastoma in the molecular subgroups, Group 3 and Group 4.
12. Combinación de los perfiles de metilación de las citosinas del Panel WNT- SHH y Panel G3-G4 para su empleo como marcador de clasificación de pacientes con meduloblastoma en los subgrupos moleculares WNT, SHH, Grupo 3 y Grupo 4. 12. Combination of the cytosine methylation profiles of Panel WNT-SHH and Panel G3-G4 for use as a classification marker for patients with medulloblastoma in the molecular subgroups WNT, SHH, Group 3 and Group 4.
13. Kit para llevar a cabo el método de la reivindicación 1 que comprende: 13. Kit for carrying out the method of claim 1 comprising:
Un set de oligonucleotidos para el análisis de los niveles de metilación de las citosinas del Panel WNT-SHH; y A set of oligonucleotides for the analysis of the cytosine methylation levels of the WNT-SHH Panel; Y
Reactivos adecuados para la metodología empleada en el análisis de la metilación de las citosinas.  Reagents suitable for the methodology used in the analysis of cytosine methylation.
14. Kit según la reivindicación 13, caracterizado porque comprende un set de oligonucléotidos seleccionados de entre los oligonucleotidos de secuencias SEQ ID NO 1-18, 48, 49, 51 , 52, 54, 55, 57, 58, 60, 61 , 63, 64, 66, 67, 69, 70 y sus combinaciones, para el análisis de los niveles de metilación de las citosinas del Panel WNT-SHH mediante secuenciación específica de ADN convertido por bisulfito. 14. Kit according to claim 13, characterized in that it comprises a set of oligonucleotides selected from oligonucleotides of sequences SEQ ID NO 1-18, 48, 49, 51, 52, 54, 55, 57, 58, 60, 61, 63 , 64, 66, 67, 69, 70 and combinations thereof, for the analysis of the levels of methylation of the cytosines of the WNT-SHH Panel by specific sequencing of bisulfite-converted DNA.
15. Kit según la reivindicación 13, caracterizado porque comprende un set de oligonucleotidos seleccionados de entre los oligonucleotidos de secuencias SEQ ID NO 1-6, 9-14, 17, 18, 35-71 y sus combinaciones, para el análisis de los niveles de metilación de las citosinas del Panel WNT-SHH mediante pirosecuenciación de ADN convertido por bisulfito. 15. Kit according to claim 13, characterized in that it comprises a set of oligonucleotides selected from oligonucleotides of sequences SEQ ID NO 1-6, 9-14, 17, 18, 35-71 and combinations thereof, for the analysis of the levels of methylation of the cytosines of the WNT-SHH Panel by pyrosequencing of bisulfite-converted DNA.
16. Kit para llevar a cabo el método de la reivindicación 2 ó 3 que comprende: 16. Kit for carrying out the method of claim 2 or 3 comprising:
Un set de oligonucleotidos para el análisis de los niveles de metilación de las citosinas de los Paneles WNT-SHH y G3-G4; y  A set of oligonucleotides for the analysis of the levels of methylation of the cytosines of Panels WNT-SHH and G3-G4; Y
Reactivos adecuados para la metodología empleada en el análisis de la metilación de las citosinas.  Reagents suitable for the methodology used in the analysis of cytosine methylation.
17. Kit, según la reivindicación 16, caracterizado porque comprende oligonucleotidos seleccionados de entre los oligonucleotidos de secuencias SEQ ID NO 1-34 y 48, 49, 51 , 52, 54, 55, 57, 58, 60, 61 , 63, 64, 66, 67, 69, 70 y sus combinaciones para el análisis de los niveles de metilacion de las citosinas del Panel WNT-SHH y G3-G4 mediante secuenciación específica de ADN convertido por bisulfito. 17. Kit according to claim 16, characterized in that it comprises oligonucleotides selected from oligonucleotides of sequences SEQ ID NO 1-34 and 48, 49, 51, 52, 54, 55, 57, 58, 60, 61, 63, 64 , 66, 67, 69, 70 and their combinations for the analysis of the levels of methylation of the cytosines of Panel WNT-SHH and G3-G4 by specific sequencing of bisulfite-converted DNA.
18. Kit según la reivindicación 16, caracterizado porque comprende los oligonucleotidos seleccionados de entre los oligonucleotidos de secuencias SEQ ID NO 1-6, 9-14, 17-79 y sus combinaciones para el análisis de los niveles de metilacion de las citosinas del Panel WNT-SHH y G3-G4 por pirosecuenciacion de ADN convertido por bisulfito. 18. Kit according to claim 16, characterized in that it comprises the oligonucleotides selected from the oligonucleotides of sequences SEQ ID NO 1-6, 9-14, 17-79 and their combinations for the analysis of the levels of methylation of the cytosines of the Panel WNT-SHH and G3-G4 by pyrosequencing of bisulfite-converted DNA.
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Citations (2)

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ES2272504T3 (en) * 2000-06-19 2007-05-01 Epigenomics Ag PROCEDURE FOR THE DETECTION OF METHYLATIONS OF CITOSINES.
WO2016142533A1 (en) * 2015-03-11 2016-09-15 Deutsches Krebsforschungszentrum Stiftung des öffentlichen Rechts Dna-methylation based method for classifying tumor species

Patent Citations (2)

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Publication number Priority date Publication date Assignee Title
ES2272504T3 (en) * 2000-06-19 2007-05-01 Epigenomics Ag PROCEDURE FOR THE DETECTION OF METHYLATIONS OF CITOSINES.
WO2016142533A1 (en) * 2015-03-11 2016-09-15 Deutsches Krebsforschungszentrum Stiftung des öffentlichen Rechts Dna-methylation based method for classifying tumor species

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