GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 11
    In: Leukemia, Springer Science and Business Media LLC, Vol. 37, No. 2 ( 2023-02), p. 500-502
    Type of Medium: Online Resource
    ISSN: 0887-6924 , 1476-5551
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2008023-2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 12
    In: Leukemia, Springer Science and Business Media LLC, Vol. 37, No. 1 ( 2023-01), p. 252-252
    Type of Medium: Online Resource
    ISSN: 0887-6924 , 1476-5551
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2008023-2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 13
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Leukemia Vol. 37, No. 4 ( 2023-04), p. 942-945
    In: Leukemia, Springer Science and Business Media LLC, Vol. 37, No. 4 ( 2023-04), p. 942-945
    Type of Medium: Online Resource
    ISSN: 0887-6924 , 1476-5551
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2008023-2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 14
    In: Leukemia, Springer Science and Business Media LLC, Vol. 37, No. 5 ( 2023-05), p. 1080-1091
    Abstract: UBA1 is an X-linked gene and encodes an ubiquitin-activating enzyme. Three somatic mutations altering the alternative start codon (M41) in UBA1 in hematopoietic precursor cells have recently been described, resulting in a syndrome of severe inflammation, cytopenias, and the presence of intracellular vacuoles in hematopoietic precursors - termed VEXAS syndrome, a predominantly male disease. Here we present a patient with clinical features of VEXAS who harbored two novel somatic variants in UBA1 (I894S and N606I). To better understand the clinical relevance and biological consequences of non-M41 ( UBA1 non-M41 ) variants, we analyzed the whole genome and transcriptome data of 4168 patients with hematological malignancies and detected an additional 16 UBA1 non-M41 putative somatic variants with a clear sex-bias in patients with myeloid malignancies. Patients diagnosed with myeloid malignancies carrying UBA1 non-M41 putative somatic variants either had vacuoles or immunodysregulatory symptoms. Analysis of the transcriptome confirmed neutrophil activation in VEXAS patients compared to healthy controls but did not result in a specific transcriptomic signature of UBA1 M41 patients in comparison with MDS patients. In summary, we have described multiple putative novel UBA1 non-M41 variants in patients with various hematological malignancies expanding the genomic spectrum of VEXAS syndrome.
    Type of Medium: Online Resource
    ISSN: 0887-6924 , 1476-5551
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2008023-2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 15
    In: Blood, American Society of Hematology, Vol. 138, No. Supplement 1 ( 2021-11-05), p. 4922-4922
    Abstract: Background: Cytomorphology is an essential method to assess disease phenotypes. Recently, promising results of automation, digitalization and machine learning (ML) for this gold standard have been demonstrated. We reported on successful integration of such workflows into our lab routine, including automated scanning of peripheral blood smears and ML-based classification of blood cell images (ASH 2020). Following this pilot project, we are focusing on an equivalent approach for bone marrow. Aim: To establish a multistep-approach including scan of bone marrow smears and detection/classification of all kinds of bone marrow cell types in healthy individuals and leukemia patients. Methods: The method includes a pre-scan at 10x magnification for detecting suitable "areas of interest" (AOI) for cytomorphological analysis, a high resolution capture of a predefinable number of AOI at 40x magnification (always using oil) and an automated object detection and classification. For all scanning tasks, a Metafer Scanning System (Zeiss Axio Imager.Z2 microscope, automatic slide feeder SFx80 and automated oil disperser) from MetaSystems (Altlussheim, GER) was used. To generate training data for AOI detection, 37 bone marrow smears were scanned at 10x magnification. 6 different quality classes of regions (based on number and distribution of cells) were annotated by hem experts using polygons. In total, 185,000 grid images were extracted from the annotated regions and used for training a deep neural network (DNN) to distinguish the 6 quality classes and to generate a position list for a high resolution scan (40x magnification). In addition, we scanned the labeled AOI of 68 smears at 40x magnification, acquiring colour images (2048x1496 pixels) of bone marrow cell layers. Each single cell was labeled by human investigators using rectangular bounding boxes (in total: 47,118 cells in 511 images). We set up a supervised ML model, using the labeled 40x images as an input. We fine-tuned the COCO dataset pre-trained YOLOv5 model with our dataset and evaluated using 5-fold cross valuation. To reduce overfitting, image augmentation algorithms were applied. Results: Our first DNN was able to detect (10x magnification) and capture (40x magnification) AOI in bone marrow smears, sorted by quality and in acceptable time spans. Average time for the 10x pre-scan was 6 min. From the resulting position list, the 50 positions with highest quality values were acquired at an average of 1:30 min. Our second, independent DNN was able to detect nucleated cells at 94% sensitivity and 75% precision in unlabeled bone marrow images (40x magnification). In this model, we overweighted recall over precision (5:1) to avoid missing any objects of interest, assuming that false positive labels could be corrected by human investigators when reviewing digital images. For the classification of single cells, a third independent DNN will be necessary. Actually, different approaches are being tested, including our existing blood cell classifier and a former collaborative bone marrow classification model based on a training set of 100,000 annotated bone marrow cells. Depending on these results, new training data for generation of a completely new model could be assessed. The two existing models enable a fully automated digital workflow including scan of bone marrow smears and delivery of single cell image galleries for human classification already now. Conclusion: We here present solutions for multiple-DNN-based tools for bone marrow cytomorphology. They allow working digitally and remotely in routine diagnostics. Final solutions will offer single cell classifications and galleries for human review and include real time training of respective classifier models with dynamic datasets. Figure 1 Figure 1. Disclosures Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2021
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 16
    In: Blood, American Society of Hematology, Vol. 138, No. Supplement 1 ( 2021-11-05), p. 273-273
    Abstract: Background: In AML and ALL the application of WHO classification and ELN guidelines requires a combination of cytogenetics and targeted sequencing for specific mutations to determine the diagnostic and prognostic subgroup. WGS and WTS have emerged as comprehensive techniques that allow the simultaneous analysis and identification of all genetic alterations in a single approach with possible turnaround times of 1 week. Aim: Evaluate the accuracy of WGS and WTS in providing all relevant genetic information in a clinical setting. Patients and Methods: The cohort comprised 738 AML, 293 BCP-ALL and 124 T-ALL. The diagnosis was established following WHO guidelines. WGS (100x, 2x151bp) and WTS (50 Mio reads, 2x101bp) were performed on a NovaSeq instrument. Variants were called with Strelka2, Manta and GATK using a tumor w/o normal pipeline, fusions with Arriba, STAR-Fusion and Manta. Results: The combination of WGS and WTS detected all chromosomal and molecular abnormalities in the AML and ALL cohorts relevant for disease stratification and prognostication as identified by chromosome banding analysis (CBA) and targeted panel sequencing (TPS). A very high concordance between CBA and WGS was revealed for the detection of balanced structural variants (SV) with the added benefit of WGS to also detect cytogenetically cryptic rearrangements (i.e.: ETV6-MN1, NUP98-KDM5A), which all were confirmed either by FISH or RT-PCR. Fusion calling by WTS identified 96% of the WHO subtype defining rearrangements and detected 20 additional fusion transcripts relevant for disease stratification (e.g. EP300-ZNF384, TCF3-HLF) including 9 fusion transcripts that led to prognostic reassignment or could serve as a potential treatment target. Breakpoints of unbalanced SV can occur in repetitive sequences of the genome, hampering the detection by WGS. However, adding copy number alteration (CNA) calls to the analyses allows also reliable identification of unbalanced SV. WGS outperformed CBA in cases with insufficient in vitro proliferation due to suboptimal pre-analytics (i.e. longer transport time) and identified 36 chromosomal aberrations in 12 cases with CBA not evaluable. WGS's independence of in vitro cell proliferation was most impactful in ALL: 40 T-ALL cases showed a normal karyotype according to CBA. WGS detected SVs in 16 (40%) and CNAs in 20 (50%) of these cases, confirming the normal karyotype for only 9 samples. In the BCP-ALL cohort, CNV analysis identified 29 low hypodiploid and 16 high hyperdiploid karyotypes, 6 of which were missed by CBA. Due to the higher resolution and unrestricted, genome-wide assessment, WGS detected relevant gene deletions (RB1, ERG, PAX5, CDKN2A, IKZF1, ETV6, BTG1) in 59% of ALL cases, providing additional diagnostic and prognostic information. In the AML cohort CBA and WGS detected 795 CNA concordantly. In addition WGS called 54 CNA with size 1-5 MB (below the detection limit of CBA), i.e. 3 BCOR deletions in inv(3)(q21q26) cases and 67 CNA with size & gt; 5 MB, which were missed by CBA. 35 CNA were missed by WGS due to small clone sizes (median 6% as determined by FISH). WGS detected copy neutral loss of heterozygosity (CN-LOH) in AML most frequently on 21q (n=17), 4q (n=15), 13q (n=15), 11q (n=13) and in T-ALL on 9p (n=19), mostly encompassing CDKN2A/B deletions. Expression profiling provided additional diagnostic information for 57 ALL cases (41 BCR-ABL1-like, 16 DUX4 rearranged) that can only insufficiently be obtained by WGS or CBA. WGS reliably detected all gene mutations with a VAF & gt; 15% (n = 647) identified by TPS encompassing especially all mutations in genes relevant for WHO diagnosis and prognostication. 26/171 mutations with a VAF & lt; 15% were missed by WGS. Evaluation of WGS data for 121 genes recurrently mutated in hematologic neoplasms revealed an additional 2 mutations per sample on average (range: 0-9) which might qualify as targets for therapy. Conclusions: WGS and WTS provide all necessary genetic information to accurately determine the diagnostic and prognostic subgroup according to WHO and ELN guidelines in AML and ALL. Compared to today's gold standards, these novel methods provide a comprehensive genome wide characterization with higher resolution that directly identifies genes of impact, offering the basis for targeted treatment selection and monitoring of residual disease. Both can be implemented with automated analysis pipelines, consequently reducing time and error rates. Figure 1 Figure 1. Disclosures Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2021
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 17
    In: Blood, American Society of Hematology, Vol. 140, No. Supplement 1 ( 2022-11-15), p. 8676-8677
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2022
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 18
    In: Blood, American Society of Hematology, Vol. 140, No. Supplement 1 ( 2022-11-15), p. 1128-1129
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2022
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 19
    In: Blood, American Society of Hematology, Vol. 136, No. Supplement 1 ( 2020-11-5), p. 17-18
    Abstract: Background: In the latest revision of WHO classification (2017) the new category `myeloid neoplasms with germline predisposition´ was introduced. Pathogenic germline (GL) mutations predisposing to hereditary hematological malignancies (HHMs) have been described in various genes and have largely been identified in families, but may also occur sporadically (Godley, Blood Adv 2019; Rio-Machin et al., Nat Commun 2020). It is estimated that 4-9% of adults with myeloid malignancies have GL predisposition and it is discussed that HHMs could even be more common (Sung and Babushok, Blood 2020). Here, we performed a screening for putative GL variants in the predisposition genes DDX41, ETV6 and GATA2 in a large cohort of 1,228 patients with sporadic AML or MDS. Aim: To determine the frequency of genetic variants in the predisposition genes DDX41, ETV6 and GATA2 in patients with AML or MDS and to characterize the mutational patterns in patients carrying putative GL variants in these genes. Patients and Methods: Between 02/2019 and 01/2020 a total of 1,228 patients were diagnosed with AML or MDS by cytomorphological analysis (475 de novo AML, 647 MDS, 60 s-AML from MDS, 46 MDS/AML; M: 753, F: 475; median age: 74 [20-96]) and analyzed by next-generation sequencing for DDX41, ETV6, GATA2 and additional genes associated with AML or MDS (Figure 1). DNA was isolated from bone marrow or peripheral blood; sequencing was performed on NovaSeq after NextFlex library preparation (Illumina, ILMN, San Diego, CA) and hybrid capture according to manufacturer's protocol (IDT Inc. Coralville, IA). Data was analyzed with Pisces and Pindel (for FLT3-ITD) (available via BaseSpace, ILMN) using a minimum of 3% sensitivity. All changes except for synonymous mutations and known polymorphisms were declared as genetic variants. Results: We identified 73/1,228 patients (5.9%) with genetic variants in DDX41, 28 (2.3%) with variants in ETV6 and 50 (4.1%) with variants in GATA2. Aiming to estimate the proportion of patients carrying a putative GL variant, a variant allele frequency (VAF) higher than 0.3 was considered to be presumptive of a GL origin. We identified 64/1,228 patients (5.2%) with a putative GL variant in DDX41. 37/64 patients harbored DDX41 variants that were previously described as causal GL variants, like for example p.M1I (12/37) or p.D140fs (4/37), and consistent with other studies 43/64 (67.2%) had an additional DDX41 mutation with a lower VAF ( & lt;0.3) suggesting somatic acquisition of the second mutation (27/43: p.R525H) (Sébert et al., Blood 2019). 13/1,228 patients (1.1%) were found to have a putative GL variant in ETV6. Two of them carried a p.R353Q mutation which was recently presumed to be a rare GL variant contributing to myeloid malignancy susceptibility (Li et al., Leukemia 2020). 26/1,228 patients (2.1%) carried a putative GL variant in GATA2, including the already in the context of HHMs described mutation p.P41A (3/26) (Holme et al., Br J Haematol 2012). In total, 102/1,228 patients (8.3%) diagnosed with AML or MDS carried a putative GL variant in one of the predisposition genes DDX41, ETV6 or GATA2. Patients with presumed GL variants were about the same age as patients without (mean: 72 vs. 71 years). The mutational patterns for the 102 patients are illustrated in Figure 1. Genes that were most often additionally mutated were ASXL1, DNMT3A, SRSF2 and TET2. For ETV6 and GATA2, all but one of the patients carrying a putative GL variant harbored at least one genetic variant in an additional gene. In contrast, 16/64 patients (25.0%) carrying a presumed GL variant in DDX41 (and in 12 cases a second somatic DDX41 mutation) had no further genetic variants in other well-known AML- or MDS-related genes indicating that DDX41 and especially the gain of a second somatic mutation drives leukemogenesis, while for ETV6 and GATA2 additional hits are required. Conclusions: Our data support the hypothesis that GL mutations in predisposition genes could be more common in sporadic cases of AML or MDS than anticipated highlighting the importance of systematic testing for these variants, irrespective of family history or age. Identifying GL mutations can be important for clinical management, including donor choice for allogeneic stem cell transplantation. Routine workflows should be adapted to improve the identification of variants in predisposition-associated genes and to facilitate confirmation of GL origin, e.g. by analyzing reference material. Figure 1 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 ...
  • 20
    In: Blood, American Society of Hematology, Vol. 136, No. Supplement 1 ( 2020-11-5), p. 16-17
    Abstract: Background: Genomic alterations are a hallmark of hematological malignancies and comprise small nucleotide variants, copy number alterations and structural variants (SV). SV lead to the co-localization of remote genomic material resulting in 2 different scenarios: 1. breakpoints are located within 2 genes leading to a chimeric fusion gene and a fusion transcript, 2. breakpoints are located outside of genes, frequently placing one nearby gene under the influence of the regulatory sequences of the partner, leading to a deregulated - usually increased - transcription. Aim: The frequency of fusion transcripts was determined across hematological entities in order to 1) identify recurrent partner genes across entities, 2) evaluate the specificity of fusion transcripts and genes involved in fusions for distinct entities. Cohort and Methods: Whole transcriptome sequencing (WTS) was performed in 3,549 patients in 25 different hematological entities (table). 101 bp paired-end reads were produced on a NovaSeq 6000 system (Illumina, San Diego, CA) with a yield between 35 and 125 million paired reads per sample. Potential fusions were called using 3 different callers (Arriba, STAR-Fusion, Manta), only fusions called by at least 2 callers, validated by whole genome sequencing (data available for all cases) and with at least one protein coding partner were kept for further analyses. Reciprocal fusion transcripts were counted as one fusion event. Results: In total 1,309 fusion transcripts were identified in 932 of 3,549 (26.3%) patients. 221 patients showed & gt; 1 fusion (2 fusions: 150, 3: 36, & gt;3: 35). 806 distinct fusion transcripts were divided into recurrent fusions (n=50) and unique fusions, i.e. found only in 1 case (n=756). Out of 932 patients with at least 1 fusion, 541 (58%) patients harbored a minimum of one recurrent fusion. The proportion of patients harboring any or a recurrent fusion varied substantially between different entities with high frequencies for both in CML (96.5%/96.5%), B-lineage ALL (53.1%/41.3%), AML (42.8%/31.2%), and T-lineage ALL (35.3%/12.6%). In several myeloid entities low fusion frequencies were observed (e.g. PMF, MDS/MPN-U, MDS, figure A). No fusion transcripts were detected in ET. Strikingly, fusions were detected in a substantial proportion of cases with lymphoid neoplasms but only very few occurred recurrently (e.g. T-PLL: 47.8%/4.3%, FL: 39.3%/4.9%, figure A). With regard to age, only patients with AML and T-ALL harboring recurrent fusions were significantly younger than corresponding cases without recurrent fusions (59 vs 71 yrs, p & lt;0.0001; 35 vs 38 yrs, p=0.02). Only in AML patients with unique fusions were older (70 vs 66 yrs, p=0.02), while no age differences were observed between cases with and without unique fusions in other entities. 23/50 (46%) of the recurrent fusions were specific for one entity (12 in myeloid, 11 in lymphatic entities), while the other 54% (27/50) were observed in 2 to 7 different entities. Of these 27 recurrent fusions, only 16 fusions were shared between myeloid and lymphatic entities, while 10 were restricted to myeloid and one fusion to lymphatic entities (figure B). In total 1,270 different genes were involved in the 806 distinct fusions, indicating a broad spectrum of potential functional impact. 54 genes were involved only in recurrent fusions, 27 genes in both recurrent and unique fusions, while 1,189 genes were solely involved in unique fusions. Four genes involved in recurrent fusions and 32 genes involved in unique fusions are FDA approved drug targets (Human Protein Atlas). Only 16% (199/1270) of the genes were involved in more than one fusion: 3 genes (ETV6, KMT2A, RUNX1) in 14 fusions, 2 genes (ABL1, BCR) in 11 fusions, 16 genes in 4 to 10 fusions, 38 genes in 3 fusions, 140 in 2 fusions. Several genes frequently involved in fusions in hematological malignancies (e.g. ABL1, ETV6, KMT2A) and 78/1189 genes only involved in unique fusions were also reported to be partners in fusions in non-hematological malignancies. Conclusions: As known, in CML and acute several leukemias a high proportion of patients harbor fusions of which many occur recurrently, suggesting a substantial pathogenic impact and, thus, requiring detection in a diagnostic work-up. In BCR-ABL1 negative chronic myeloid malignancies few fusions were observed while lymphoma patients carry frequently non-recurrent fusions with so far unknown impact on pathogenesis and prognosis. 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 ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...