Method of classification in molecular subgroups of patients with medulloblastoma (Machine-translatio

专利摘要:
Method for the classification in molecular subgroups of patients with medulloblastoma. The present invention relates to the use of the combination of the methylation profiles of the cytokines of the panel WNT-SHH and Panel G3-G4 as a marker of classification of patients with medulloblastoma in the molecular subgroups WNT, SHH, Group 3 and Group 4. Likewise, the method for the classification of patients with medulloblastoma in one of said molecular subgroups based on the analysis of the methylation of the cytosines of the Panels WNT-SHH and G3-G4 is contemplated. Finally, the kit for carrying out the classification method of the invention is contemplated. (Machine-translation by Google Translate, not legally binding)
公开号:ES2690160A1
申请号:ES201730701
申请日:2017-05-17
公开日:2018-11-19
发明作者:Cinzia Emilia Lavarino；Soledad GOMEZ GONZALEZ
申请人:Hospital Sant Joan de Deu；
IPC主号:

专利说明:

Classification method in molecular subgroups of patients with medulloblastoma Field of the Invention
The present invention has its field of application within the health sector, mainly in the sectors of "Pediatric Oncology" and "Molecular Biology". 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 classifying a patient with medulloblastoma into one of the molecular subgroups WNT, SHH, Group 3 and Group 4. Background of the invention
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). In developed countries, MB is the most common cause of cancer death in children over 1 year of age (European detailed mortality database (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 metastases develop in the central nervous system (cranial or medullary), while 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. 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. 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 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 patient's age at 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). Patients under 3 years of age, with evidence of residual tumor (≥1.5cm2) 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 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 Tumors, Lyon: IARC 2000). Desmoplastic / nodular medulloblastoma (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).
The clinical-pathological behavior of MB is a reflection of the underlying genetic genetic characteristics of the tumor. During the last several years several


Genomic studies (massive genome sequencing, DNA transcriptome and methyloma analysis and study of chromosomal alterations) 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. Natu re Reviews Cancer 2012, 12: 818834; 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 archival 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: 449457; Thompson EM et al. Prognostic value 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).
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 MB


WNT 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 WNT MBs is CTNNB1 (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 3 subgroups 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).
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 family of proto-oncogenes GFI1 and GFI1B by means of a mechanism of "hijacking" 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 11 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).
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.
In 2010, at a consensus conference in Boston, the Medulloblastoma Working Group recognized the subgroups of MB, 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. The 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 Tumors of the Central Nervous System. WHO / IARC Classification of Tumors, 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).
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 a marker panel analysis as an alternative methodology using immunohistochemical techniques (Northcott PA et. Medulloblastoma comprising four distinct molecular variants. Journal of Clinical Oncology 2011, 29 (11): 1408-1414) but it has been demonstrated which is difficult to standardize in neuropathology laboratories.
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 (Illumina Infinium Human Methylation 450K BeadChip array (HM450K)) to analyze the complete genome (DNA) methylation profile of tumors.
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 Sick Children’s Hospital in Toronto identified 22 genes whose expression levels allow MBs to be classified
(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


quantify the 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.
The HM450k microarray interrogates the methylation status of more than 450,000 cytosines throughout the entire genome. It covers 96% percent of the cytosinaguanine 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 make it necessary to use computational technology, bioinformatics methods (programming and application of algorithms / programs for analysis, classification, data mining and data visualization, between 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 high density microarray technology (Illumina 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 Cancer 2012, 12: 818834; 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 archival 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: 449457; Thompson EM et al. Prognostic value 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 Panel WNT-SHH and Panel 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" (iCpG). 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 rate of


Low variation, 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). These modifications occur naturally in the cell but can be modulated by various factors such as age, environment and diseases. Recent studies are highlighting the key role 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 methylomes at base resolution show widespread epigenomic differences. Nature (2009) 462: 315-22).
The most studied epigenetic modification in humans is DNA methylation. Methylation analysis has undergone a revolution over the past decade, especially since the adaptation of microarray technology to the study of methylation and the emergence of next-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 methylomes 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).
There are multiple possible approaches to the analysis of methylation, based on different enzymatic and chemical strategies (treatment with sodium bisulfite (NaHSO3),


treatment with restriction enzymes and affinity enrichment, among others).
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.
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.
Among 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) or sequencing methods (bisulfite sequencing and bisulfite pyrosequencing).
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 stored at -80ºC (FF, fresh frozen) or fixed in 10% buffered formalin and embedded in paraffin (FFPE, from


English formalin fixed, paraffin-embedded).
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. Description of the figures
Figure 1.-Standardization, quality control and filtering of raw methylation data generated by high density microarray (microarray) Illumina Human Methylation BeadChip 450K technology. 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.
Β value (β-value): estimation of the level of methylation by the ratio between the value of the methylated and the non-methylated allele (methylated / non-methylated + methylated +100).
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 plot) 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. (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 Panel WNT-SHH in others


normal and tumor tissues.
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: atypical teradoid / rhabdoid tumor (atypical teradoid / rhabdoid tumor); NB: neuroblastoma; GN: ganglioneuroma.
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 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 the methylation values of the cytosines of Panel G3-G4 in other normal and tumor tissues.
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: atypical teradoid / rhabdoid tumor (atypical teradoid / rhabdoid tumor); NB: neuroblastoma; GN: ganglioneuroma.
Figure 5.-Validation of Panel WNT-SHH and Panel G3-G4 through the use of 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.
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) Subgroup SHH and (C) Subgroup no


WNT / no-SHH.
Figure 7.- Graphic example of the methylation levels of the WNT-SHH Panel cytosines obtained by bisulfite pyrosequencing in F / FF and FFPE medulloblastoma tissue DNA. 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 further test.
10 easily applicable that existing classification systems, that is reproducible and with a good cost-effectiveness in clinical practice.
In the latest revision of the World Health Organization (WHO) classification of tumors of the central nervous system published in 2016, the following 15 genetically defined entities were introduced: 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 / non-SHH” MBs.
In response to the need to apply this classification system in clinical practice
20 of patients with MB to determine the clinical risk and to define the most appropriate treatment for each patient, in the present invention two panels of cytosines are defined which, in combination, act as an effective molecular classification marker of patients with MB in four Molecular subgroups: WNT, SHH, Group 3 and Group 4.
For this, 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 associated with
30 meaningfully and accurately with each of the WNT, SHH, and non-WNT / non-SHH subgroups. 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 .


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. Table 1. WNT-SHH panel.
Subgroup Cytosine IDIllumina IDChromosomeStart positionFinal position
WNT WNT1_MBcg255420419124982087124982088
WNT WNT2_MBcg24280645174863690048636901
WNT WNT3_MBcg02227036165042532950425330
no-WNT / no-SHH N-WS1_MBcg18849583143283615732836158
no-WNT / no-SHH N-WS2_MBcg198288692171552304171552305
no-WNT / no-SHH N-WS3_MBcg012683457138603645138603646
SHH SHH1_MBcg1033341616844474844475
SHH SHH2_MBcg109594406148701916148701917
SHH SHH3_MBcg129253552234386471234386472
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, NWS2_MB, N-WS3_MB, SHH1_MB, SHH2_MB and SHH3_MB.
10 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
15 (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. When values of high methylation are observed in the cytosines SHH1_MB and SHH2_MB and low in SHH3_MB, this pattern defines the SHH subgroup univocally and directly. High values in NWS1_MB and N-WS2_MB, and low values 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 panel can be seen in the schematic table described below (Table 2)


WNT-SHH.
Table 2. Reference methylation pattern of the cytosines that constitute the WNT-SHH Panel for the WNT, SHH and non-WNT / non-SHH subgroups.
Cytosine ID no-WNT / no-SHH
WNT1_MB -
WNT2_MB -
WNT3_MB +
N-WS1_MB +
N-WS2_MB +
N-WS3_MB -
SHH1_MB -
SHH2_MB -
SHH3_MB +
Cytosine ID SHH
WNT1_MB -
WNT2_MB -
WNT3_MB +
N-WS1_MB -
N-WS2_MB -
N-WS3_MB +
SHH1_MB +
SHH2_MB +
SHH3_MB -
Cytosine ID WNT
WNT1_MB +
WNT2_MB +
WNT3_MB -
N-WS1_MB -
N-WS2_MB -
N-WS3_MB +
SHH1_MB -
SHH2_MB -
SHH3_MB +
10 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%).
Also, 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 provisionally included in the non-WNT / non-SHH subgroup of WHO.
20 Table 3. Panel G3-G4
Subgroup Cytosine IDIllumina IDChromosomeStart PositionFinal Position
Group number 3 Gr3-A_MBcg1354894612123350077123350078
Group number 3 Gr3-B_MBcg05679609123067192630671927
Group number 3 Gr3-C_MBcg09929238177856091678560917
Group number 3 Gr3-D_MBcg240444788145035191145035192


Group 4 Gr4-A_MBcg08129331177856047878560479
Group 4 Gr4-B_MBcg10400652194699651646996517
Group 4 Gr4-C_MBcg125655858105235943105235944
Group 4 Gr4-D_MBcg16167052194699634746996348
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.
5 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.
10 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.
15 Tumors with a methylation pattern with high values (≥ 75%) 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 , are associated specifically and uniquely with the subgroup Group 3 of MBs. While low values in the cytosines Gr3-A_MB, Gr3-B_MB, Gr3-C_MB, Gr3-C_MB,
20 Gr3-D_MB, Gr4-A_MB, Gr4-B_MB, Gr4-C_MB and Gr4-D_MB are indicators of a tumor belonging to the subgroup Group 4. In the schematic table described below (Table 4) you can see the reference methylation patterns for the G3-G4 panel.
Table 4. Reference methylation pattern of the cytosines that constitute Panel G3-G4 for the 25 subgroups Group 3 and Group 4.
Cytosine ID Group number 3
Gr3-A_MB +
Gr3-B_MB +
Gr3-C_MB +
Gr3-D_MB +
Gr4-A_MB +
Cytosine ID Group 4
Gr3-A_MB -
Gr3-B_MB -
Gr3-C_MB -
Gr3-D_MB -
Gr4-A_MB -


Gr4-B_MB +
Gr4-C_MB +
Gr4-D_MB +
Gr4-B_MB -
Gr4-C_MB -
Gr4-D_MB -
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%).
5 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 subgroups Molecules 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 Panel
10 SHH, the cytosine methylation profile of Panel G3-G4 (and its different combinations) is contemplated for use as a marker for the classification of patients with MB in the molecular subgroups Group 3 and Group 4.
The analysis of the methylation pattern of the proposed cytosines allows contrasting the
15 levels of differential methylation between MB subgroups with different clinical behavior, which allows classifying tumors according to their clinical evolution, and establishing the most appropriate treatment for each patient.
The authors of the invention have shown that the analysis of said markers (Panel
20 WNT-SHH and Panel G3-G4) is easily applicable in clinical practice and improves the cost-effectiveness of the methods proposed to date.
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.
25 WNT, SHH and non-WNT / non-SHH group comprising the following steps:
a) Analysis of the 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 of the
30 same, 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 methylation levels of the cytosines analyzed in the WNT-SHH panel, according to the reference values in the Table
2.
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 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, or a combination thereof, which form panel G3-G4, in the DNA extracted from the biological sample isolated from the patient, and
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.
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. Combined analysis of the cytosine methylation levels 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. 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 that form the WNT-SHH or G3-G4 panels.
For the realization of the classification method of the invention, a biological sample is isolated from a patient and the DNA is extracted by means of


Conventional protocols, DNA treatment and subsequent analysis of the levels of methylation of each of the cytosine of interest. The data obtained are compared with the cytosine reference methylation panel to establish the molecular subgroup to which the tumor belongs.
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.
In 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 Panel WNT-SHH, Panel G3-G4 or 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).
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, from the English formalin fixed paraffin embedded).
In a particular embodiment of the method of classification of the invention, the biological sample is fresh tumor tissue (F). In this case, the MB classification method 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


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.
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. 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 state of methylation of a sequence of interest.
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).
The conversion of DNA with sodium bisulfite (NaHSO3) 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 double DNA helix and the specific binding of two oligonucleotides (primers) that flank the region to be amplified and serve as primers to initiate the synthesis of the fragment. 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 PCR amplification of the fragments


genetics 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.
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.
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 Natl Acad Sci USA, 74 (12): 5463-5467) . These lack the hydroxyl group of carbon 3 ’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) (França 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 (França 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, G


black, C blue and A green), so 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 DNA synthesis reaction. 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 the reproducibility, specificity and accuracy of the analysis, comparable with high resolution methodologies. Pyrosequencing of bisulfite treated DNA allows a quantitative and precise analysis of methylation based on sequencing by synthesis.
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.
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.


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:
- A set of oligonucleotides suitable for the analysis of
methylation levels of the cytosines of the WNT-SHH panel; Y
- All reagents suitable for the methodology used
in the analysis of the methylation of these cytosines. 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 for control cytosines, and combinations thereof.
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.
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:
- A set of oligonucleotides suitable for the analysis of
levels of cytosine methylation of the WNT-SHH Panel and
G3-G4, in combination; Y
- Reagents suitable for the methodology used in the


Methylation analysis of these cytosines.
In 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 and 72-79, specific for the problem cytosines of the G3-G4 panel, and the oligonucleotides of sequences SEQ ID NO 48-71, specific for 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 MgCl2 at optimal concentrations, in addition to the optimized dNTPs for the methodology.


Finally, 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 panel WNT-SHH and / or G3- G4
EXAMPLES
Example 1
Identification of the cytosine panel with differential methylation capable of discriminating the molecular subgroups of medulloblastoma.
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.
Methylation patterns
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 (Illumina 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.
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.
Table 5. Medulloblastoma databases used in the study


GEO * ID TitleSamplesLink
GSE54880 Microarray-based DNA methylation profiling of medulloblastoma and normal cerebellum samples106https://www.ncbi.nlm.nih.gov/geo /query/acc.cgi acc=GSE54880
GSE54880 Microarray-based DNA methylation profiling of medulloblastoma and normal cerebellum samples169https://www.ncbi.nlm.nih.gov/geo /query/acc.cgi acc=GSE54880
* Gene Expression Omnibus (GEO): repository of genomic databases. https://www.ncbi.nlm.nih.gov/geo
Table 6. Databases of tumors and normal tissues used in the study
GEO ID TitleSamplesLink
GSE30654 Recurrent Variations in DNA Methylation in Human Pluripotent Stem Cells and their Differentiated Derivatives40https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE30654
GSE36278 Methylation data from glioblastoma tumor samples126https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE36278
GSE44684 DNA methylation data from pilocytic astrocytoma tumor samples and normal cerebellum controls54https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE44684
GSE45353 Epigenomic Alterations Define Lethal CIMP-positive Ependymomas of Infancy48https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE45353
GSE50022 Illumina Infinium 450K array data for Diffuse Intrinsic Pontine Glioma24https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE50022
GSE50798 Differences in DNA methylation between human neuronal and glial cells are concentrated in enhancers and non-CpG sites24https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE50798
GSE54719 DNA methylation changes at CpG and non-CpG sites are associated with development and clinical behavior in neuroblastoma41https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE54719
GSE54880 Microarray-based DNA methylation profiling of medulloblastoma and8https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE54880


normal cerebellum samples
GSE55712 Genome wide methylation profliling of pediatric glioblastomas98https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE55712
GSE65306 Comprehensive genomic analysis of relapse neuroblastoma14https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE65306
GSE70460 The genomic and epigenomic landscape of atypical teratoid rhabdoid tumors150https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE70460
GSE70737 An integrative analysis of reprogramming in human isogenic system identified a clone selection criterion7https://www.ncbi.nlm.nih.gov/geo/quer and / acc.cgi acc = GSE70737
* Gene Expression Omnibus (GEO): repository of genomic databases. https://www.ncbi.nlm.nih.gov/geo
The study was based on raw genomic data (files called Intensity Data files -iDat) included in the GSE54880 database, generated from a total of 5 106 primary medulloblastomas (study cohort) obtained fresh at the time of diagnosis (Table 5). A single database was generated from the iDat files of the study cohort. Next, we proceeded with the normalization, quality control and filtering of methylation data, as previously described (Gomez S et al. DNA methylation fingerprint of neuroblastoma reveals new biological and clinical 10 insights. Genomics Data 2015, 5 : 360-363 Gomez S et al. DNA methylation fingerprint of neuroblastoma reveals 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 were used
15 (http://www.bioconductor.org/).
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 dispersion of the raw data using density plots (diagram of distribution of variability) (Figure 1A) and scatter plots (diagram of dispersion) (Figure 1B) in order to identify samples with methylation values that move away from the average. Normalization was carried out using the SWAN function (subset-quantile within


array normalization) 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 1C).
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 reveals new biological and clinical insights. Genomics Data 2015, 5: 360-363 Gomez S et al. DNA methylation fingerprint of neuroblastoma reveals 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.
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).
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.
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.
An analysis of the distribution of the variability (densisty plot) 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).
Likewise, another unsupervised method called hierarchical clustering was applied. 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.
For the unsupervised hierarchical clustering analysis, 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).
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).
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 differential methylation patterns and reduce them to a lower number of cytosines, losing the least amount of information 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.
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.
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.
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.


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
5 each of the subgroups, as shown in Table 7 and Figure 3B.
10 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)
No-WNT / no-SHH SHHWNT
WNT1_MB 8%9%88%
WNT2_MB 2%2%85%
WNT3_MB 92%91%12%
N-WS1_MB 89%16%17%
N-WS2_MB 80%eleven%fifteen%
N-WS3_MB 9%88%87%
SHH1_MB 14%90%13%
SHH2_MB 6%88%18%
SHH3_MB 96%eleven%96%
15 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
20 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
25 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 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).
The 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.rproject.org/), as previously described (Queirós AC et al. Leukemia (2015) 29,598605).
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 (29 combinations) to define which cytosines and how many of these were necessary to obtain the best possible classification. Both the nine cytosines and all the possible combinations (29 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 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.
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).
To identify the second DNA methylation pattern that is significantly and specifically associated with the subgroups Group 3 and Group 4, the following were applied


Selection criteria: 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 subgroup, obtaining the differential methylation pattern of 8 cytosines that constitute Panel G3 -G4 (Table 8).
5 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.
10 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).
15 Tumors with a methylation pattern with high values (≥ 75%) 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 , are associated specifically and uniquely 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
20 subgroup Group 4 (Table 8 and Figure 4B).
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
Gr3-A_MB 82%26%
Gr3-B_MB 84%22%
Gr-3-C_MB 79%18%
Gr-3-D_MB 86%28%
Gr-4-A_MB 66%14%
Gr-4-B_MB 72%19%
Gr-4-C_MB 72%7%
Gr-4-D_MB 78%27%


LDA prediction of subgroups 3 and 4 using methylation data from the study cohort of the cytosines of Panel G3-G4. Likewise, all possible combinations (28 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.
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).
Conclusion example 1:
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.
Example 2
Validation of a Medulloblastoma classification method using DNA methylation microarray databases.
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 Group 4 in MB.
Validation: database of primary MB microarrays fixed in formalin and included in paraffin.
For this, the authors of the invention 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 were obtained using high density microarray technology (Illumina 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 archival 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.
The study carried out by the authors of the invention was based on the raw genomic data (files called iDat files of the English Intensity Data files) of the validation cohort (Table 5 and 6). A single database was generated from the iDat files. Then, the normalization, quality control and filtering of the methylation data was carried out, according to the procedures described in Example 1.
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.
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 to the original WNT-SHH Panel pattern (Table 7), in the validation cohort each subgroup was specifically and univocally associated with a bimodal methylation pattern


differential of cytosines (Table 9).
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-SHH WNTSHH
F / FF FFPE F / FFFFPE F / FFFFPE
WNT1_MB 8%eleven%9%eleven%88%79%
WNT2_MB 2%2%2%5%85%66%
WNT3_MB 92%90%91%90%12%twenty-one%
N-WS1_MB 89%89%16%24%17%28%
N-WS2_MB 80%76%eleven%16%fifteen%twenty%
N-WS3_MB 9%12%88%87%87%83%
SHH1_MB 14%fifteen%90%87%13%19%
SHH2_MB 6%6%88%86%18%19%
SHH3_MB 96%96%eleven%13%96%95%
5 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
10 classification data previously published 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).
15 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).
20 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
F / FF FFPE F / FFFFPE
Gr3-A_MB 82%82%26%33%
Gr3-B_MB 84%79%22%24%


Gr-3-C_MB 79%76%18%24%
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-C_MB 72%61%7%14%
Gr-4-D_MB 78%65%27%28%
To assess the classification capacity, the LDA classification model of Panel G3-G4 was applied to samples classified as non-WNT / non-SHH (n = 115) by the WNT-SHH Panel. It was observed how Panel G3-G4 was able to effectively differentiate the
5 two entities Group 3 and Group 4 with 97% concordance (41/42 G3 and 71/73 G4) with the classification data published previously 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).
10 Conclusions Example 2: 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.
15 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 genetic entities Group 3 and Group
Four.
20 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 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
The proposed classification method is applicable to this type of biological material.
Example 3
Validation of a primary MB classification method using various methodologies


and independent cohorts
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.
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.
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.
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.
Two isolated groups of independent primary MB samples were used.
 Group 1: 96 MB frozen at -80ºC (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)
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 methylated positive control and four as a non-methylated negative control (Table 11).
Table 11. Control cytosines (Normal Peripheral Blood n = 40).
subgroup id cytosineid illuminachromosomestart startfinal startfrommethylation average


normal (n = 40)
positive control CP1_MBcg134585611013455838913455839097%
positive control CP2_MBcg19602374one158334851583348697%
positive control CP3_MBcg01724941713219277813219277997%
positive control CP4_MBcg122035437754848797548488097%
negative control CN1_MBcg2288596561070771151070771164%
negative control CN2_MBcg0585482651387749181387749192%
negative control CN3_MBcg0558416681455825471455825482%
negative control CN4_MBcg06319390one1563081831563081842%
* 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
5 the manufacturer's instructions. DNA quantification was performed by absorbance reading at 260nm wavelength, on 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.).
10 For those formalin-fixed and paraffin-included tumors (FFPE), the DNA was extracted from the samples using the QIAamp DNA FFPE kit (Qiagen Technologies) or similar, following the manufacturer's instructions.
15 The initial step of the molecular techniques used to analyze the state of methylation is the conversion of DNA with sodium bisulfite (NaHSO3). For this, the starting point was 1ng - 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 article
20 by author Darst RP et al. Bisulfite Sequencing of DNA. Current Protocols of Molecular Biology (2010) doi: 10.1002 / 0471142727.mb0709s91.


Table 12. Bisulfite reaction components.
Component High concentration samples (1ng-2µg) Volume per reaction (µl)Low concentration samples (1-500 ng) Volume per reaction (µl)
DNA solution Variable (maximum 20 µl)Variable (maximum 40 µl)
RNA-handle free water VariableVariable
Bisulfite mixture (dissolved), see step 1 8585
DNA protection buffer 35fifteen
Total volume 140140
Table 13. Thermocycler conditions for bisulfite conversion.
He passed WeatherTemperature
Denaturalization 5 minutes95 ° C
Incubation 25 minutes60ºC
Denaturalization 5 minutes95 ° C
Incubation 85 minutes (1 h 25 minutes)60ºC
Denaturalization 5 minutes95 ° C
Incubation 175 minutes (2 h 55 minutes)60ºC
Standby mode Undefined20ºC
Selective amplification of DNA fragments of interest by polymerase chain reaction (PCR).
As a first step we proceeded with the bioinformatic design of specific primers for
10 methylated alleles and unmethylated alleles (Tables 14, 15 and 16). The GenBank sequence (https://www.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 the programs for design and analysis of
15 primers such as: Methyl Primer Express Software (Thermo Fisher Scientific), MethPrimer (http://www.urogene.org/methprimer2/), BiSearch (http://bisearch.enzim.hu/), among others.
Table 14. Table of BSP primers of the WNT-SHH Panel


Cytosine ID Illumina IDPrimerPCR productTª banding
WNT1_MB cg25542041(SEQ ID NO 1)26158
(SEQ ID NO 2)
WNT2_MB cg24280645(SEQ ID NO 3)25460
(SEQ ID NO 4)
WNT3_MB cg02227036(SEQ ID NO 5)19658
(SEQ ID NO 6)
N-WS1_MB cg18849583(SEQ ID NO 7)16155
(SEQ ID NO 8)
N-WS2_MB cg19828869(SEQ ID NO 9)19658
(SEQ ID NO 10)
N-WS3_MB cg01268345(SEQ ID NO 11)22058
(SEQ ID NO 12)
SHH1_MB cg10333416(SEQ ID NO 13)23158
(SEQ ID NO 14)
SHH2_MB cg10959440(SEQ ID NO 15)20760
(SEQ ID NO 16)
SHH3_MB cg12925355(SEQ ID NO 17)16258
(SEQ ID NO 18)
Table 15. Table of BSP primers of Panel G3-G4
Cytosine ID Illumina IDPrimerPCR productTª banding
Gr3-A_MB cg13548946(SEQ ID NO 19)22058
(SEQ ID NO 20)
Gr3-B_MB cg05679609(SEQ ID NO 21)18958
(SEQ ID NO 22)
Gr3-C_MB cg09929238(SEQ ID NO 23)36558
(SEQ ID NO 24)
Gr3-D_MB cg24044478(SEQ ID NO 25)20660
(SEQ ID NO 26)
Gr4-A_MB cg08129331(SEQ ID NO 27)18160
(SEQ ID NO 28)
Gr4-B_MB cg10400652(SEQ ID NO 29)23060
(SEQ ID NO 30)
Gr4-C_MB cg12565585(SEQ ID NO 31)23258
(SEQ ID NO 32)
Gr4-D_MB cg16167052(SEQ ID NO 33)25660
(SEQ ID NO 34)


Table 16. Control primers for BSP
Cytosine ID Illumina IDPrimerPCR productTª banding
CP1_MB cg13458561(SEQ ID NO 48)21058
(SEQ ID NO 49)
CP2_MB cg19602374(SEQ ID NO 51)15958
(SEQ ID NO 52)
CP3_MB cg01724941(SEQ ID NO 54)20158
(SEQ ID NO 55)
CP4_MB cg12203543(SEQ ID NO 57)22360
(SEQ ID NO 58)
CN1_MB cg22885965(SEQ ID NO 60)19460
(SEQ ID NO 61)
CN2_MB cg05854826(SEQ ID NO 63)20558
(SEQ ID NO 64)
CN3_MB cg05584166(SEQ ID NO 66)22660
(SEQ ID NO 67)
CN4_MB cg06319390(SEQ ID NO 69)18860
(SEQ ID NO 70)
Subsequently, the PCR reagent mixture was prepared (Table 17).
Table 17. Mixture reagents for PCR.
Reagent 1x (µl)
Water adjust for a final volume of 24µl
PCR buffer 5
MgCl2 1.5
dNTP 1.5
First 5 '(10µM) 0.5
First 3 '(10µM) 0.5
DNA polymerase 0.13


In 0.2 ml PCR tubes 24 µl of reagent mixture (Table 17) and 1 µl of the DNA converted with corresponding bisulfite (at 50ng / µl) were dispensed. Each reaction had its negative control in which sterile water was added instead of a DNA sample.
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. At 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.
Sequencing of bisulfite converted DNA
From the amplified product of bisulfite-converted DNA, the cytosine methylation patterns of interest (Panel WNT-SHH and Panel G3-G4) were analyzed by specific automated sequencing of bisulfite-converted DNA (BSP).
As a first step, the PCR amplified product was purified using the ExoSAP-IT® kit (USB-Affymetrix) or similar, following the supplier's instructions. Next, 2.5 µl of PCR product and 1 µl 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, 22 µl of water was added to the purified product.
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 µl of PCR product purified by ExoSAP-IT® was added the following reagents:
Table 18. Components of the sequencing reaction.
Reagent 1x (µL)
Water adjust for a final volume of 10uL
Big Dye Buffer one
Big dye 451
Fw or Rv probe 0.5


* Big Dye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) or similar.
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.
Subsequently, 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 µl of the sequencing product was added to the AutoSeqTM G-50 © column of the kit (GE Healthcare 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, the samples were analyzed 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 that, as a result of the BSP analysis of the cytosines of the validation database corresponding to the WNT-SHH Panel, they showed a methylation pattern equivalent to the selected / original cytosines. Similarly, validation cytosines were associated in a significant and specific way (100% concordance) with the three genetic entities defined by WHO (2016): WNT, SHH and Non-WNT / non-SHH Group of MB. The same results were obtained from both DNA extracted from tumor tissue in F / FF and from FFPE tissue (Figure 6).
The analysis using the BSP methodology of the cytosines corresponding to Panel G3-G4, showed a moderate specificity of the methylation pattern and 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 the Group
Four.


Pyrosesequencing by bisulfite From the DNA converted with sodium bisulfite, the levels of methylation of the cytosines of interest (Panel WNT-SHH and Panel G3-G4) were also quantified using the bisulfite pyrosequencing method. For this, samples of
5 DNA extracted from both tumor tissue in F / FF and in FFPE.
In order to analyze the cytosines of interest, biotinylated (5'-terminal) primer pairs were designed, specific for methylated and nonmethylated alleles (Table 19, 20 and 21). For this, the PyroMark Assay Design (Qiagen Technologies) or similar tool was used.
10 Table 19. Pyrosequencing primers for the WNT-SHH Panel
D Cytosine Illumina IDPrimer TypePrimers / Probes 5 ’- 3’
WNT1_MB cg25542041Primer(SEQ ID NO 1)
Primer + Biotin (SEQ ID NO 2)
Probe (SEQ ID NO 39)
WNT2_MB cg24280645Primer + Biotin(SEQ ID NO 3)
Primer (SEQ ID NO 4)
Probe (SEQ ID NO 40)
WNT3_MB cg02227036Primer(SEQ ID NO 5)
Primer + Biotin (SEQ ID NO 6)
Probe (SEQ ID NO 41)
NWS1_MB cg18849583Primer(SEQ ID NO 35)
Primer + Biotin (SEQ ID NO 36)
Probe (SEQ ID NO 42)
NWS2_MB cg19828869Primer(SEQ ID NO 9)
Primer + Biotin (SEQ ID NO 10)
Probe (SEQ ID NO 43)
NWS3_MB cg01268345Primer(SEQ ID NO 11)
Primer + Biotin (SEQ ID NO 12)
Probe (SEQ ID NO 44)
SHH1_MB cg10333416Primer(SEQ ID NO 13)
Primer + Biotin (SEQ ID NO 14)
Probe (SEQ ID NO 45)
SHH2_MB cg10959440Primer(SEQ ID NO 37)
Primer + Biotin (SEQ ID NO 38)
Probe (SEQ ID NO 46)
SHH3_MB cg12925355Primer(SEQ ID NO 17)


Primer + Biotin (SEQ ID NO 18)
Probe (SEQ ID NO 47)
Table 20. Pyrosequencing primers for Panel G3-G4
Cytosine ID Illumina IDPrimer TypePrimers / Probes 5 ’- 3’
Gr3-A_MB cg13548946Primer(SEQ ID NO 19)
Primer + Biotin (SEQ ID NO 20)
Probe (SEQ ID NO 72)
Gr3-B_MB cg05679609Primer(SEQ ID NO 21)
Primer + Biotin (SEQ ID NO 22)
Probe (SEQ ID NO 73)
Gr3-C_MB cg09929238Primer(SEQ ID NO 23)
Primer + Biotin (SEQ ID NO 24)
Probe (SEQ ID NO 74)
Gr3-D_MB cg24044478Primer(SEQ ID NO 25)
Primer + Biotin (SEQ ID NO 26)
Probe (SEQ ID NO 75)
Gr4-A_MB cg08129331Primer(SEQ ID NO 27)
Primer + Biotin (SEQ ID NO 28)
Probe (SEQ ID NO 76)
Gr4-B_MB cg10400652Primer(SEQ ID NO 29)
Primer + Biotin (SEQ ID NO 30)
Probe (SEQ ID NO 77)
Gr4-C_MB cg12565585Primer(SEQ ID NO 31)
Primer + Biotin (SEQ ID NO 32)
Probe (SEQ ID NO 78)
Gr4-D_MB cg16167052Primer(SEQ ID NO 33)
Primer + Biotin (SEQ ID NO 34)
Probe (SEQ ID NO 79)
Table 21. Control primers for pyrosequencing
Cytosine ID ID illuminaType of primer5’-3 ’primers
CP1_MB cg13458561Primer(SEQ ID NO 48)
Primer + Biotin (SEQ ID NO 49)
Probe (SEQ ID NO 50)
CP2_MB cg19602374Primer(SEQ ID NO 51)
Primer + Biotin (SEQ ID NO 52)
Probe (SEQ ID NO 53)

CP3_MB cg01724941Primer(SEQ ID NO 54)
Primer + Biotin (SEQ ID NO 55)
Probe (SEQ ID NO 56)
CP4_MB cg12203543Primer(SEQ ID NO 57)
Primer + Biotin (SEQ ID NO 58)
Probe (SEQ ID NO 59)
CN1_MB cg22885965Primer(SEQ ID NO 60)
Primer + Biotin (SEQ ID NO 61)
Probe (SEQ ID NO 62)
CN2_MB cg05854826Primer(SEQ ID NO 63)
Primer + Biotin (SEQ ID NO 64)
Probe (SEQ ID NO 65)
CN3_MB cg05584166Primer(SEQ ID NO 66)
Primer + Biotin (SEQ ID NO 67)
Probe (SEQ ID NO 68)
CN4_MB cg06319390Primer(SEQ ID NO 69)
Primer + Biotin (SEQ ID NO 70)
Probe (SEQ ID NO 71)
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 the Table
22
Table 22. Composition of the reagent mixture for the amplification of a bisulfite converted DNA region for pyrosequencing.
Component Volume per reactionFinal concentration
PyroMark PCR Master Mix, 2x 12.5 µl1x
CoralLoad Concentrate, 10x 2.51x
25 mM MgCl2 (optional) Variable≥1.5 mM
Solution-Q, 5x (optional) 5 µl1x
Primer A / Primer B Variable / variable0.2 µM / 0.2 µM
RNA-handle free water Variable-
Total volume (after the addition of the DNA template) 25
The PCR tubes were introduced into the thermal cycler and proceeded according to the conditions described in Table 23, standard protocol subject to optimization.


Once the amplification was finished, the immobilization of the PCR products with Streptavidin Sepharose High Performance (GE Helthcare) or similar microspheres was carried out before proceeding with the pyrosequencing analysis.
The master sample was prepared with the "Streptavidin Sepharose High Performance" microspheres and DNA immobilization reagents according to the data in Table 24.
10 70 µl of master mix was added to each well of a PCR plate next to 10 µl of biotinylated PCR (total volume per well 80 µl) and the plate was centrifuged (1,400rpm) for 5-10 minutes, according to standard protocol subject to optimization.
Then, the samples were prepared prior to the pyrosequencing analysis in PyroMark Q24 (Qiagen) or similar. In the PyroMark Q24 plate (Qiagen) or similar, 40 µl of alliniament buffer and 0.5 µl of
20 specific primer for the PCR products. The PyroMark Q96 Plate 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 station


of emptiness. 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
5 85 ° C, 2 minutes.
Next, 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),
10 according to the manufacturer's recommendations (PyroMark Q24 User Manual, Qiagen).
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, we proceeded to document and interpret the results of methylation quantification in the
15 pyrogram / histogram obtained (Tables 25, 26 and Figure 7).
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, the 20 validation cytosines were significantly and specifically associated with the three genetic entities defined by WHO (2016): WNT, SHH and non-WNT / non-SHH Group of MB. 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
25 classify all the samples with a concordance of 100% (Tables 25 and 26).
Table 25. Example of the classification capacity of the WNT-SHH Panel in tumor tissue in F / FF.
Samples no-WNT / no-SHHSHHWNTWNT-SHH Panel Prediction
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% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
SHH 0%100%0% SHH
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
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

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
WNT 0%0%100% WNT
WNT 0%0%100% WNT
WNT 0%0%100% WNT
WNT 0%0%100% WNT
Table 26. Example of the classification capacity of the WNT-SHH Panel in tumor tissue in FFPE.
Samples No-WNT / no-SHHSHHWNTWNT-SHH panel prediction
No-WNT / no-SHH FFPE 100%0%0%No-WNT / no-SHH
No-WNT / no-SHH FFPE 100%0%0%No-WNT / no-SHH
No-WNT / no-SHH FFPE 100%0%0%No-WNT / no-SHH
SHH FFPE 0%100%0%SHH
WNT FFPE 0%0%100%WNT
5 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 panel
10 WNT-SHH.
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
15 the 8 samples of FFPE.
The following tables show the summary of the results of the analysis of the levels of


Methylation of the G3-G4 panel by bisulfite pyrosequencing in F / FF and FFPE medulloblastoma tissue DNA (Tables 27 and 28).
Table 27. Example of the classification capacity of Panel G3-G4 in tumor tissue in F / FF
Samples Group number 3Group 4G3-G4 Panel Prediction
Group number 3 100%0% Group number 3
Group 4 97%3%Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 100%0% Group number 3
Group number 3 0%100%Group 4
Group number 3 0%100%Group 4
Group 4 25%75% Group 4
Group 4 4%96% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4


Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Group 4 0%100% Group 4
Table 28. Example of the classification capacity of Panel G3-G4 in tumor tissue in FFPE.
Samples Group number 3Group 4G3-G4 panel prediction
Group 3 FFPE 100%0% Group 3 FFPE
Group 3 FFPE 100%0% Group 3 FFPE
Group 3 FFPE 100%0% Group 3 FFPE
Group 3 FFPE 100%0% Group 3 FFPE
Group 3 FFPE 0%100%Group 4 FFPE
Group 3 FFPE 0%100%Group 4 FFPE
Group 4 FFPE 0%100% Group 4 FFPE
Group 4 FFPE 0%100% Group 4 FFPE
In the column to the left of Tables 27 and 28 you can see the molecular classification
5 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 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.
10 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.


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 keeping the cytosine methylation pattern of the proposed markers stable, Panel WNT-SHH and Panel G3-G4,
5 The proposed classification method is applicable to this type of biological material.
Conclusions Example 3:
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 for
10 establish 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.
It was also demonstrated as the differential methylation pattern of cytosines that
15 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 demonstrated that the methylation profile of the cytosines of tissue interest
Tumor fixed in formalin and included in paraffin is comparable to tissue obtained 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.

权利要求:
Claims (32)
[1]
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 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 Panel WNT-SHH, 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 methylation of the WNT-SHH Panel cytosines analyzed in a), according to reference values (Table 2 ).
[2]
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 biological sample isolated from the patient, and
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]
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. Combined analysis of the cytosine methylation levels of the WNT-SHH panel, or a combination thereof, and of Panel G3-G4, or a combination thereof, in 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 methylation of the

cytosines of Panel WNT-SHH and Panel G3-G4 analyzed in A), according to reference values (Tables 2 and 4).
[4]
Four. Method according to any of the preceding claims, wherein the biological sample used is tumor tissue.
[5]
5. Method according to claim 4, wherein the viable tumor cell content in the tumor tissue sample is at least 70%.
[6]
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]
7. Method according to any one of claims 1-6, wherein the analysis of cytosine methylation levels is carried out by specific sequencing of bisulfite-converted DNA.
[8]
8. Method according to any one of claims 1-6, wherein the analysis of the levels of cytosine methylation is carried out by means of the pyrosequencing technology of bisulfite-converted DNA.
[9]
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]
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.
[11]
eleven. 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]
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]
13. Kit for carrying out the method of claim 1 comprising:
 An oligonucleotide set for the analysis of the cytosine methylation levels of the WNT-SHH Panel; Y
 Reagents suitable for the methodology used in the analysis of cytosine methylation.
[14]
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 their combinations, for the analysis of the levels of methylation of the cytosines of the WNT-SHH Panel by specific sequencing of bisulfite-converted DNA.
[15]
fifteen. 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 methylation levels of the cytosines of the WNT-SHH Panel by pyrosequencing of bisulfite-converted DNA.
[16]
16. Kit for carrying out the method of claim 2 or 3 comprising:  An oligonucleotide set for the analysis of the cytosine methylation levels of Panels WNT-SHH and G3-G4; Y
 Reagents suitable for the methodology used in the analysis of cytosine methylation.
[17]
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]
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 combinations thereof for the analysis of the levels of methylation of the cytosines of Panel WNT-SHH and G3-G4 by pyrosequencing of bisulfite-converted DNA.


 Beta

 β- Value
FIG. 1A

Control: BISULFITO CONVERSION I Control: BISULFITO CONVERSION II
6 8 10121416 6810121416 Log2 intensity Log2 intensity
FIG. 1 B

Density
 Beta
5 4 3 2 1 0
 β- Value
 FIG. 1 C

[0]
 0.0 0.1 0.2 0.3 0.4 Standard deviation
 FIG. 2A 

MB_Group 3 MB_WNT MB_Group 4 MB_SHH
- 10
 -10 -20
FIG. 2B

 MB_Group 3
Color code
 MB_WNT
 FIG. 2 C

 FIG. 3A 

1.00 Subgroup WNT WNT1_MB 1.00WNT2_MB1.00WNT3_MB
[0]
0.750.750.75
β- Value
[0]
0.500.500.50
[0]
0.250.250.25
[0]
0.000.000.00
Gr3 Gr4SHHWNT Gr3Gr4SHHWNT Gr3Gr4SHHWNT
1.00 SHH Subgroup. SHH1_MB 1.00SHH2_MB1.00SHH3_MB
[0]
0.750.750.75
β- Value
[0]
0.500.500.50
[0]
0.250.250.25
[0]
0.000.000.00
Gr3 Gr4SHHWNT Gr3Gr4SHHWNT Gr3Gr4SHHWNT
1.00 1.00 Non-WNT / non-SHH subgroup. N-WS1_MB N-WS2_MB1.00N-WS3_MB
[0]
0.750.750.75
β- Value
[0]
0.500.500.50
[0]
0.250.250.25
[0]
0.000.000.00
Gr3 Gr4SHHWNT Gr3Gr4SHHWNT Gr3Gr4SHHWNT
 FIG. 3B 

WNT1_MBWNT2_MBWNT3_MBN-WS1_MBN-WS2_MBN-WS3_MBSHH1_MBSHH2_MBSHH3_MB
FIG. 3C

PC1
 FIG. 4A 

Gr3-A_MBGr3-B_MBGr3-C_MBGr3-D_MB
Gr3 Gr4 Gr3 Gr4 Gr3 Gr4 Gr3 Gr4
FIG. 4B

Gr3-A_MBGr3-B_MBGr3-C_MBGr3-D_MBGr4-A_MBGr4-B_MBGr4-C_MBGr4-D_MB
FIG. 4C

- twenty
 FIG. 5A

[0]
0.5
 FIG. 5B 

F / FF FFPE
WNT1_MB WNT2_MB WNT3_MB WNT1_MBWNT2_MB WNT3_MB
FIG. 6A

F / FF FFPE
SHH1_MB SSH2_MB SHH3_MB SHH1_MBSHH2_MB SHH3_MB
FIG. 6B

F / FF FFPE
FIG. 6C

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引用文献:

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公开号 | 申请日 | 公开日 | 申请人 | 专利标题

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PCT/ES2018/070347| WO2018211160A1|2017-05-17|2018-05-10|Method for classifying medulloblastoma patients into molecular sub-groups|