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  • American Society of Hematology  (2)
  • Narita, Atsushi  (2)
  • 1
    In: Blood, American Society of Hematology, Vol. 134, No. Supplement_1 ( 2019-11-13), p. 2973-2973
    Abstract: Background Juvenile myelomonocytic leukemia (JMML) is a rare myelodysplastic/ myeloproliferative neoplasm that occurs during infancy and early childhood. The clinical course of the disease varies widely. The majority of children require allogenic hematopoietic stem cell transplantation (HSCT) for long term survival, but the disease will eventually resolve spontaneously in ~15% of patients. Previous studies have identified clinical and molecular risk factors in JMML. More recently, three groups independently discovered that genome-wide methylation profiling using 450K Illumina array revealed that the high methylation (HM) subgroup was significantly associated with poor survival compared to the low methylation (LM) subgroup (Murakami 2018 Blood, Stieglitz 2017 Nat. Commun., Lipka 2017 Nat. Commun.). 450K could be a standard assay for stratification of JMML. However, it is now unavailable because the manufacture replaced it with EPIC array. Here, we developed a next-generation sequencing-based clinical test recapitulate 450K clustering results using the digital restriction enzyme analysis of methylation (DREAM) method (Jelinek 2012 Epigenetics). Patients and Methods We studied 99 children (67 boys and 32 girls) with JMML. All the patients were included in our previous publications. First, we assessed JMML samples with DREAM. Briefly, genomic DNA was sequentially cut with two enzymes SmaI and XmaI recognizing the same sequence, CCCGGG sites in DNA. Enzyme-treated DNA was then used to generate sequencing libraries according to the Illumina protocols, and run on an Illumina Hiseq 2500. We assessed 10 JMML samples with reduced representation bisulfite sequencing (RRBS) (Meissner 2005 Nucleic Acids Res.). In brief, purified genomic DNA was digested by the methylation-insensitive restriction enzyme MspI to generate short fragments that contain CpG dinucleotides at the ends. The CpG-rich DNA fragments (40-220 bp) were size selected, subjected to bisulfite conversion, PCR amplified and end sequenced on an Illumina Genome analyzer. Results We analyzed 99 samples using the DREAM with 8.87 (4.09-16.35) million reads (median, [range]), and determined methylation level in 62,525 (52,356-75,185) CpG sites (median [range] ). We observed a strong correlation between DREAM methylation ratio and 450K beta-value of overlapping CpG sites (Pearson r2 = 0.95 [0.913-0.962], median [range] ). We performed unsupervised consensus clustering with DREAM methylation data of 7,704 CpG sites within ±1 kb from TSS on autosomal chromosomes detected in ≥95% of the samples with imputation of the missing data using the median of each CpG site methylation level. Clustering identified two distinct subgroups, the HM subgroup (n = 35) and the LM subgroup (n = 64), matching 95% (94 of 99) with the 450K clustering results. The HM subgroup patients showed significantly poorer 5-year OS than the LM subgroup patients (41.9% [95% confidence interval {CI}], 25.3%-57.6%) vs. 71.4% [95% CI, 56.2%-82.1%] ; P = 0.00345). Discrepancies in the clustering results between DREAM and 450K were observed in only 5 patients (2 survived and 3 died); all 5 patients were reclassified as those with LM with DREAM from being HM with 450K. We also performed RRBS methylation analysis on 10 patients. Unsupervised consensus clustering using promoter-associated 4,971 CpG sites measured with RRBS identified HM (n = 5) and LM (n = 5) subgroups and completely matched with the classification made using DREAM and 450K. Then, we developed a prediction model of the methylation subgroups using a machine-learning program. We selected 85 CpG sites from 7,704 CpG sites used for unsupervised clustering of the DREAM assay that showed a distinct difference in the average methylation level ( 〉 0.3) between the HM and LM subgroups of the learning cohort (n = 70) and developed a support vector machine (SVM) model. As a validation cohort, we analyzed the remaining 29 JMML samples with a SVM model and confirmed a high matching rate with 450K clustering results (100%, 29 of 29). Conclusions We could develop a methylation test for JMML using the DREAM assay. Both the unsupervised clustering analysis and SVM model could repeat the result of 450K-based methylation classification, i.e., the HM and LM subgroups. The relatively lower cost of the DREAM assay (US$200/sample) enabled us to incorporate methylation classification in JMML in most settings. Disclosures No relevant conflicts of interest to declare.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2019
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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  • 2
    In: Blood, American Society of Hematology, Vol. 136, No. Supplement 1 ( 2020-11-5), p. 9-9
    Abstract: BACKGROUND: Inherited bone marrow failure syndromes (IBMFSs) are a heterogeneous group of genetic disorders characterized by bone marrow failure, physical anomalies, and various kinds of organ complications. In addition to classical IBMFSs, such as Fanconi anemia, Diamond-Blackfan anemia, Dyskeratosis congenita, Shwachman-Diamond syndrome, and familial platelet disorders, many types of unclassified IBMFSs are reported. Over 100 genes are considered causative genes; however, the precise genetic diagnosis of IBMFSs remains challenging. We developed a capture-based target sequencing method for IBMFSs that covers more than 180 associated genes. Our system achieved genetic diagnosis for 225 (35%) of 738 patients between 2013 and 2018. However, the causative gene remained unknown for 513 (65%) patients, and further genetic analysis of these "target-negative" cases was necessary to achieve a precise diagnosis. METHODS: We performed whole exome sequencing (WES) for patients who were "target-negative" but strongly suspected of having IBMFS based on the following clinical characteristics: physical or organ anomalies (skin, nail, hair, skeletal, growth, cardiac, lung, liver, or genitourinary), family history of hematological disorder, young age (≤2 years), short telomere length ( & lt;-2.0 SD), and hyper sensitivity to the chromosome breakage test. A sequencing library was prepared using the SureSelect Human All Exon 50Mb kit (Agilent Technologies, Santa Clara, CA, USA) and it was sequenced using the HiSeq2000 platform (Illumina, San Diego, CA, USA), according to manufacturers' instructions. The candidate germline variants were detected through our Genomon-exome analysis pipeline. With mean coverage of 100×, ≥ 85% of all protein coding bases were covered at 20× or more. RESULTS: Among the 513 "target-negative" cases, 166 patients were evaluated, of whom 17 patients' parents were also analyzed in a trio-based analysis. New pathogenic variants were identified in 18 of the 166 (11%) patients according to the American College of Medical Genetics (ACMG) guidelines, of which 5 variants were revealed to be de novo. Diagnostic variants were identified in FANCF, SRP54, RPL19, RPL5, RTEL1, RUNX1, MECOM, CDC42, GNE, SLNF14 (all n = 1). In addition to IBMFS-associated genes, causative genes for congenital hemolytic anemia (G6PD, PKLR), inborn error of metabolism (SLC46A1), and primary immune deficiency (NFKB2, LRBA) are also identified (all n = 1). Moreover, loss-of-function mutation of ADH5 gene are identified in three patients that seems to be associated to novel IBMFSs. On the other hand, no pathogenic variant in GATA2, ERCC6L2, LIG4, and SAMD9/SAMD9L genes that are reported as unclassified IBMFSs in Europe and United States are identified in our cohort. CONCLUSION: Our findings support the utility of WES (especially trio-based analysis) as a diagnostic tool for IBMFSs. Furthermore, genetic background of IBMFSs in East Asia seems to be different from that of Europe and United States. Disclosures No relevant conflicts of interest to declare.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2020
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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