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Pediatric acute myeloid leukemia (AML) represents a major cause of childhood leukemic mortality, with only a limited number of studies investigating the molecular landscape of the disease. Here, we present an integrative analysis of cytogenetic and molecular profiles of 75 patients with pediatric AML from a multicentric, real-world patient cohort treated according to AML Berlin-Frankfurt-Münster protocols. Targeted next-generation sequencing of 54 genes revealed 17 genes that were recurrently mutated in >5% of patients. Considerable differences were observed in the mutational profiles compared with previous studies, as BCORL1, CUX1, KDM6A, PHF6, and STAG2 mutations were detected at a higher frequency than previously reported, whereas KIT, NRAS, and KRAS were less frequently mutated. Our study identified novel recurrent mutations at diagnosis in the BCORL1 gene in 9% of the patients. Tumor suppressor gene (PHF6, TP53, and WT1) mutations were found to be associated with induction failure and shorter event-free survival, suggesting important roles of these alterations in resistance to therapy and disease progression. Comparison of the mutational landscape at diagnosis and relapse revealed an enrichment of mutations in tumor suppressor genes (16.2% versus 44.4%) and transcription factors (35.1% versus 55.6%) at relapse. Our findings shed further light on the heterogeneity of pediatric AML and identify previously unappreciated alterations that may lead to improved molecular characterization and risk stratification of pediatric AML.
Pubmed ID: 37088137 RIS Download
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Database to store and display somatic mutation information and related details and contains information relating to human cancers. The mutation data and associated information is extracted from the primary literature. In order to provide a consistent view of the data a histology and tissue ontology has been created and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology or gene and displayed as a graph, as a table or exported in various formats.
Some key features of COSMIC are:
* Contains information on publications, samples and mutations. Includes samples which have been found to be negative for mutations during screening therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.
* Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumours, recurrences, metastases and cancer cell lines.
Database as central repository for both single base nucleotide substitutions and short deletion and insertion polymorphisms. Distinguishes report of how to assay SNP from use of that SNP with individuals and populations. This separation simplifies some issues of data representation. However, these initial reports describing how to assay SNP will often be accompanied by SNP experiments measuring allele occurrence in individuals and populations. Community can contribute to this resource.
View all literature mentionsA Python based language and execution environment for make-like workflows. The system supports the use of automatically inferred multiple named wildcards (or variables) in input and output filenames.
View all literature mentionsArchive of aggregated information about sequence variation and its relationship to human health. Provides reports of relationships among human variations and phenotypes along with supporting evidence. Submissions from clinical testing labs, research labs, locus-specific databases, expert panels and professional societies are welcome. Collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about submitter, and other supporting data. Alleles described in submissions are mapped to reference sequences, and reported according to HGVS standard.
View all literature mentionsInternational collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes
View all literature mentionsData analysis service to predict the functional consequences of known and unknown variants.
View all literature mentionsCommercial instrument and chemical vendor. Developer and manufacturer of specialized technological products for life science research and clinical diagnostics markets.
View all literature mentionsSoftware toolkit for filtering and manipulating annotated files. After annotation, the software's filter function can find relevant genomic variants in large data files.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
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