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  • 1
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    Molecular Classification of AML-MRC Reveals a Distinct Profile and Identifies MRC-like Patients with Poor Overall Survival
    American Society of Hematology ; 2019
    In:  Blood Vol. 134, No. Supplement_1 ( 2019-11-13), p. 2735-2735
    Details
    In: Blood, American Society of Hematology, Vol. 134, No. Supplement_1 ( 2019-11-13), p. 2735-2735
    Abstract: Background: AML with myelodysplasia related changes (AML-MRC) is as specific WHO category with poor prognosis. It requires ≥ 20% of blasts, and (1) the history of MDS or MDS/MPN, or (2) "MDS related cytogenetic abnormalities", or (3) multilineage dysplasia. Drugs such as Vyxeos® have been approved by FDA and EMA only for treatment of t-AML or AML-MRC. However, counting blasts or grading dysplasia in clinical routine is hampered by limited reproducibility due to different levels of expertise and small phenotypic alterations, challenging upfront treatment decisions. Cytogenetics is not available in all cases and has 5-10 days of turnaround time (TAT). In contrast, next-generation sequencing (NGS) panels for AML are now broadly available at faster TAT. Aim: (1) Use machine learning to define a molecular AML-MRC signature; (2) compare the impact of conventional WHO definitions and molecular factors on classification and outcome. Patients and Methods: Gold standard routine AML diagnosis was performed on 739 cases. Overall survival (OS) data was available for 619 patients. Amplification-free whole genome sequencing was performed on HiSeqX and NovaSeq with median coverage of 106x. Gender-matched reference DNA was used for unmatched normal variant calling with Strelka2. Pindel was used for FLT3-ITD. For variant classification, we applied a GnomAD cutoff of 0.0005 and filtered on protein-truncating and (likely) pathogenic variants from databases. Results: According to WHO standards 165/739 (22%) cases fulfilled MRC criteria (96 male; 69 female). The non-MRC cohort (n=574) represents a heterogeneous AML population incl. the WHO defined recurrent cytogenetic abnormalities or t-AML (301 male, 273 female). Median age was higher in the MRC cohort (73 [22-90] vs. 64 [18- 93] years, p 〈 .001) and OS was significantly shorter (median 6 vs. 23 months, p 〈 .001). Mutation analysis was limited to 73 frequently mutated genes, in order to allow application of our model on prospective diagnostic cases analyzed by common routine panels. In the MRC group, up to seven mutations were found per patient and an average of 2.7 genes per patient were mutated. The most frequently mutated gene in AML-MRC was TP53 (62/165, 38%) as expected by the inclusion of complex karyotypes. TP53 mutations were associated with shorter OS in the MRC cohort (median: 3 vs. 11 months, p =.001). We used machine learning (ML) approaches to identify with LASSO regression and 10-fold cross-validation the most informative features to distinguish between MRC and patients without MRC. The dataset was randomly divided into a training (90%) and test set (10%) and the procedure was repeated 500 times to cover all the variance in the dataset and to extract the most reliable factors. Factors with the highest weight on AML-MRC prediction were mutations in TP53, RUNX1, SETBP1, splicing factors and epigenetic regulators, and absence of mutations in NPM1, CEBPA and others (s. figure). In order to allow our model to be used in a routine diagnostic workflow, we also used the genes identified by ML but classified mutations by a simpler point system (≥2 points as cutoff for MRC, s. figure). This allowed us to identify 83% (137/165 by ML) and 70% (116/165 by points) of cases currently defined as MRC solely by molecular genetics. Including cytogenetic data and patient's history in an informed genetic model results in 99% (164/165 by ML) and 96% (159/165 by points) of true positive MRC definition. However, the molecular models classified 112 (ML) and 80 (points) of the 574 non-MRC cases, as being AML-MRC. Even after excluding AML with recurrent cytogenetic abnormalities and t-AML, 14% (82/574 DL) or 11% (63/574 points) show a MRC-like molecular profile. In both models MRC-like patients had dismal outcome analogous to AML-MRC (median OS: 6 months for both) and significantly inferior to remaining non-MRC patients (6 vs. 35 months, s. figure). Conclusions: (1) Using patients' history and genetic information instead of morphology allow to identify 96-99% of AML-MRC as defined in WHO today. In the future, extended NGS panels (e.g. incl. fusion gene detection) will allow fast and standardized AML-MRC classification even without chromosome banding analysis. (2) The molecular MRC-like pattern can be found in 〉 10% of patients currently not classified as AML-MRC but with comparably poor OS. This suggests considering MRC treatment strategies for patients with MRC-like molecular profile. Disclosures Baer: MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Stengel:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood-2019-128234
    RVK:
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    RVK:
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2019
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  • 2
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    Comprehensive Analysis of 22 Cases with Blastic Plasmacytoid Dendritic Cell Neoplasm Reveals Mutations in DNA Repair Genes and Two Distinct Subgroups Defined By Differential Gene Expression with Prognostic Impact
    American Society of Hematology ; 2019
    In:  Blood Vol. 134, No. Supplement_1 ( 2019-11-13), p. 1234-1234
    Details
    In: Blood, American Society of Hematology, Vol. 134, No. Supplement_1 ( 2019-11-13), p. 1234-1234
    Abstract: Background: Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematological malignancy with a poor prognosis. The majority of patients with BPDCN show a complex karyotype. Recurrently mutated genes mainly include epigenetic modifiers and splicing factor genes, however a number of other mutations, many characteristic for myeloid disorders were also detected in BPDCN. Gene expression (GE) profiling revealed an aberrant activation of the NFκB pathway and high expression of BCL2. More recently, rearrangements in MYC were also reported and are associated with an even more aggressive course. However, the exact pathogenesis of BPDCN is still unclear. Aim: Comprehensive analysis of molecular mutations, structural variations and GE profile in 22 cases with BPDCN by whole genome sequencing (WGS) and whole transcriptome sequencing (WTS). Methods: WGS and WTS was performed for 22 BPDCN patients. For WGS, 151bp paired-end reads were generated on NovaSeq 6000 machines (Illumina, San Diego, CA). A mixture genomic DNA from multiple anonymous donors was used as normal controls. All reported p-values are two-sided and were considered significant at p 〈 0.05. For GE analysis, estimated gene counts were normalized and the resulting log2 counts per million were used as a proxy of gene expression in each sample. Results: Median age of the cohort was 74 years (range 16 - 89 years), the majority of patients were male (20/22). After stringent filtering, 112 genes were found recurrently mutated in 22 BPDCN patients. The most frequently mutated genes were epigenetic or splicing factor genes: TET2 (23 mutations (mut) in 15/22 cases, 68%), followed by ASXL1 (7 mut in 7/22 cases, 32%), SRSF2 (7 mut in 7/22 cases, 32%) and ASXL2 (3 mut in 3 3/22 cases, 14%). Moreover, in almost half of the cases (10/22, 45%) a mutation in at least one gene involved in DNA repair (according to Wood et al., Science 2001) was detected: ATM (4 mut in 3 cases), POLH (3 mut in 3 cases), TP53 (2 mut in 1 case), POLE (2 mut in 1 case), REV3L, WRN, MSH6, RAD51C (1 case each). Regarding structural aberrations, deletions of 5q (6/22 cases, 27%), 12p (7/22, 32%), 13q (9/22, 41%), 15q (6/22, 27%) and monosomy 9 (8/22, 36%) were frequently observed. Additionally, we detected gains of 1q and 7q in 4/22 (18%) and 3/22 (14%) cases, respectively. A deletion of TP53 (17p13) was observed in 2/22 (9%) cases. In 4/22 cases (18%) rearrangements involving MYC were observed, the translocation partners were localized on 6p21 (2 cases), 3p24 and 13q14 (1 case, each), respectively. Unsupervised clustering of the whole transcriptional profiles revealed a segregation of the cohort into two distinct groups (see Figure). Interestingly, one group was characterized by a significantly higher expression of the neutrophil-specific receptor CD177 (HNA2A, NB1, PRV1) and CD11b, indicating the presence of an activated subset of neutrophils. This was supported by the pathway enrichment analysis of the differentially expressed genes between both groups, which revealed activation of immune response as a significantly overrepresented biological process. The bimodal expression pattern of CD177 has often been linked to the presence of various SNPs such as A134T, G156A and G1333A (Moritz et al. 2010), however, analysis of the WGS data did not show such a correlation in our cohort. The CD177 gene promotor contains binding sites for the transcription factor CEBPA, which was significantly up-regulated in group 1 and might contribute to the increased expression of CD177. The expression of CD177 has also been linked to overall survival (OS) (Costa et al. 2017) and, interestingly, patients of group 1 showed a longer OS than patients of group 2 (5.9. vs. 3.6 months, p=0.012). Conclusions: (1) Our results give further insight into the complex pattern of genetic aberrations in patients with BPDCN: beside the well-described mutations in epigenetic modifiers and splicing factor genes, a large number of mutations in DNA repair genes (e.g. ATM, POLH, TP53, POLE) were detected. (2) In 18% of our cases, rearrangements involving MYC were detected. (3) GE analysis revealed two different subgroups of BPDCN, differentiated by activation of immune response genes and by the two surface markers CD11b and CD177 with an implication on OS. CD177 is well characterized as an immunotarget and recent studies suggest that CD177-mediated nanoparticle targeting might improve cancer immunotherapy (Chu et al. 2016, Miettinen et al. 2018). Disclosures Stengel: MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Baer:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood-2019-125645
    RVK:
    XA 33000
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2019
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  • 3
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    A Novel Machine Learning Based in silico Pathogenicity Predictor for Missense Variants in a Hematological Setting
    American Society of Hematology ; 2019
    In:  Blood Vol. 134, No. Supplement_1 ( 2019-11-13), p. 2090-2090
    Details
    In: Blood, American Society of Hematology, Vol. 134, No. Supplement_1 ( 2019-11-13), p. 2090-2090
    Abstract: Background: Interpreting the pathogenic potential of an amino-acid changing single nucleotide variant (SNV) in a disease related gene can be challenging, especially for rare variants for which little or no information is available in clinical databases. In silico predictors, tools that predict the functional impact of an SNV algorithmically, can be useful in this scenario, and guidelines for variant interpretation recommend their inclusion in the interpretation process. Resources such as the dbNSFP database, which contains pre-calculated prediction scores for dozens of different algorithms, are readily available today. However, individual predictors rarely come to the same conclusion, and even for well-known disease causing SNVs results can be heterogeneous or even contradictory, which complicates their interpretation. Ensemble predictors such as REVEL, MetaLR/SVM or CADD combine the knowledge/information from multiple individual sources. These predictors use machine learning methods and training sets of pre-defined pathogenic and benign SNVs to integrate individual algorithms into a single, easy to interpret score. However, current training sets are based on pathogenic germline variants, which might cause these predictors to underperform when testing somatic variants. Aim: Development of HePPy (Hematological Predictor of Pathogenicity), an ensemble in silico predictor trained on somatic disease causing variants for use in a hematological setting. Methods: We followed the approach laid out by REVEL and used 10 in silico predictor scores and 4 phylogenetic conservation scores from the dbNSFP data base to train a random forest model. Our training set consisted of 371 unique missense SNVs from 61 hematologically relevant genes that were recurrently identified (in at least 10 patients) during routine diagnostics. All were consistently and unambiguously characterized by hematological experts as either a pathogenic somatic variant (n = 268) or a benign germline variant (n = 103) using a rigorous manual classification process within a data set of 69,879 cases studied between 2005 and 2018. Model accuracy was assessed by 10-fold cross-validation and further evaluated using a test data set consisting of 335 rare missense SNVs from routine diagnostics for which control germline material (buccal swabs, finger nail clippings) from the respective patients was available. Variants originating in the germline were expected to be mainly benign (n = 123), while somatic variants were considered pathogenic (n = 212). We compared the performance of this new tool to REVEL, MetaLR/SVM, CADD and the popular individual predictors SIFT and Polyphen2 by generating receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Model implementation and analysis was performed using the R libraries "randomForest", "caret" and "pROC". Results: HePPy scores range from 0 (benign) to 1 (pathogenic) and cross-validation on the training set indicates a high accuracy of 0.968, which is also reflected by the clear separation in the distribution of obtained scores for benign and pathogenic training SNVs (see figure B). Application of the model to the test data set of rare SNVs shows that HePPy (AUC = 0.873) outperforms all other prediction tools in separating germline from somatic variants (see figure A). Surprisingly, both MetaLR (AUC = 0.717) and MetaSVM (AUC = 0.703) performed worse than the individual predictors SIFT (AUC = 0.794) and Polyphen2 (AUC = 0.821), while CADD (AUC = 0.831) and REVEL (AUC = 0.850) showed better performance. HePPy scores for somatic test variants were heavily skewed towards very high values (mean = 0.917). Germline variants had significantly lower scores (mean = 0.466), but their distribution was much more uniform than for somatic variants (see figure C). This suggests, to consider a significant proportion of the rare germline variants to have pathogenic potential. This is in line with the growing awareness of pathogenic germline variants and familial predisposition and emphasizes the importance of in silico predictions and other tools to replace the simple "tumor vs. normal" comparison. Summary: We developed HePPy, a new in silico ensemble predictor that is trained on 371 well-defined hematopathological somatic missense variants, which outperforms other currently available methods for in silico prediction in a hematological setting. Figure Disclosures Hutter: MLL Munich Leukemia Laboratory: Employment. Baer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood-2019-128488
    RVK:
    XA 33000
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2019
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  • 4
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    Analysis of Mechanisms of Blast Crisis in Chronic Myeloid Leukemia By Whole Genome Sequencing
    American Society of Hematology ; 2020
    In:  Blood Vol. 136, No. Supplement 1 ( 2020-11-5), p. 19-19
    Details
    In: Blood, American Society of Hematology, Vol. 136, No. Supplement 1 ( 2020-11-5), p. 19-19
    Abstract: Background: Thanks to tyrosine kinase inhibitors (TKIs) chronic myeloid leukemia (CML) has become a well manageable disease. This drastically changes once an individual progresses to blast crisis (BC), which carries a poor prognosis. Although progression to BC fortunately is a rare event, the mechanisms leading to transformation from chronic phase to BC are sparsely studied, which led us to perform in depth analyses of CML patients each at diagnosis (D), at molecular or hematological remission (REM) and at BC by whole genome sequencing (WGS). Aim: (1) Study chromosomal and mutational profiles at D, REM and BC (2) Identify chromosomal and molecular genetic mechanisms in progression to BC Patients and Methods: We performed in depth analyses of 11 CML patients with BC confirmed by cytomorphology. REM samples were available for 8 patients, in 1 patient 2 BC samples were sequenced. Median age at D was 59 years (range 31-70) and median time to BC was 2 years (range 0-6). Nine patients received only first line TKI and 2 patients had switched to second- or third line TKIs before or at time of BC. We sequenced DNA of bone marrow (n=17) or peripheral blood (n=14) by WGS at a median coverage of 106x and used Strelka2 for variant calling. Structural variations (SVs) were analyzed by Manta caller, copy number alterations (CNVs) were called using the GATK4 CNV calling pipeline. Results: None of the patients presented with known high risk additional chromosomal aberrations (ACA) at D (Hochhaus et al, Leukemia 2020). Using WGS at D, we found deletions in the breakpoint region of der(9)t(9;22) (n=2), der(22)t(9;22) (n=1) and a translocation involving 12p and der(22)t(9;22) (n=1). Mutations in known myeloid driver genes were found in 4 patients at D. In two patients (DNMT3A, ASXL1) mutations were present at D and BC, while in two patients three ASXL1 mutations were present at D (VAF 27%; 23% and 14%), but could not be detected at BC by WGS and more sensitive targeted sequencing. Both patients presented with complex ACAs detected both by chromosome banding analysis (CBA) and WGS at BC. Other known driver or resistance mutations were not detected in any other sample at D. We identified three mechanisms driving the transition from chronic phase to BC, the first being ABL1 resistance mutations which render one or several TKIs ineffective (n=6/11). All patients developed ABL1 mutations that conferred resistance to the TKI they were receiving. In BC we detected T315I, Y253H, F359V (n=2 each), E450K, Q252H and Q255K (n=1 each) mutations, one patient had T315I, Q255K and F259V mutation combined, all other patients had a single mutation. In one patient two BCs occurred which showed both an additional t(9;10) but differed in the ABL1 mutations: First Q252H was detected and after REM in second BC T315I was present whereas the Q252H was absent. Secondly, in 10/11 of all patients SVs or CNVs were identified in addition to t(9;22)(q34;q11) by WGS in BC. If CBA data was available it confirmed the WGS data. Patients showed an additional t(9;10) (n=1), t(10;11) (n=1), inv(16) (n=1) and CNVs involving virtually all chromosomes which were only detected in BC pointing towards a major role of chromosomal instability. Interestingly WGS detected de-novo leukemic driver mutations in BC that are described primarily in other myeloid malignancies, representing the third mechanism (n=7). Two patients gained truncating mutations in BCOR, one patient each gained a mutation in ASXL1 and CUX1, one patient 3 frameshift mutations in ETV6, another patient two mutations in WT1 and one case gained a CBFB-MYH11 rearrangement which is usually found in a subtype of AML. Conclusion: Using WGS we found three different contributing mechanisms to progression to BC in CML: 1) ABL1 resistance mutations, 2) gain of structural and copy number variations and 3) potentially rise of additional AML like mutations. Since none of these factors were identified at D and REM, a comprehensive screening is recommended to detect, at the earliest possible time point when molecular remission is lost, the drivers to BC and allow early clinical intervention such as allogeneic transplant. Disclosures No relevant conflicts of interest to declare.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood-2020-140688
    RVK:
    XA 33000
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2020
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  • 5
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    Incidential Findings of Mutations in the PIGA Gene Are Highly Specific for the Presence of PNH Clones
    American Society of Hematology ; 2021
    In:  Blood Vol. 138, No. Supplement 1 ( 2021-11-05), p. 1117-1117
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    In: Blood, American Society of Hematology, Vol. 138, No. Supplement 1 ( 2021-11-05), p. 1117-1117
    Abstract: Background: Paroxysmal nocturnal hemoglobinuria (PNH) is a hemolytic anemia associated with severe thrombophilia and characterized by complement-mediated lysis of erythrocytes lacking glycosylphosphatidylinositol (GPI)-anchored proteins. In the majority of cases, GPI deficiency is caused by somatic mutations in the PIGA gene. Presence of PNH clones is associated with acquired aplastic anemia (AA) and can be found in patients with myelodysplastic syndrome (MDS) or rarely other myeloid neoplasms (MN). Flow cytometric analysis for deficiency of GPI-anchored proteins on multiple cell lineages detects PNH clones, and PIGA mutational analysis is not mandatory to establish the diagnosis. In contrast, molecular genetic analysis of targeted gene panels is widely used in the diagnostic workup of MN. We hypothesized that the inclusion of PIGA into the myeloid gene panel could identify obscure cases with PNH clones irrespective of the initial clinical suspicion. Aim: To assess the significance of incidental findings of mutations in PIGA in the diagnostic workup of MN. Methods: 20,320 consecutive patients undergoing sequencing analysis for a confirmed or suspected MN were analyzed for the presence of mutations in the PIGA gene. Patients with confirmed PNH analyzed only for PIGA were not included, and cases with previously known PIGA mutations were excluded from further analysis. DNA was isolated from peripheral blood (PB) or bone marrow, and sequencing was performed on NovaSeq after Illumina DNA Prep for Enrichment library preparation (Illumina, San Diego, CA) and hybrid capture of a 41 gene panel including the complete coding sequence of PIGA (IDT Inc., Coralville, IA); data was analyzed with Pisces and Pindel (BaseSpace, Illumina). Flow cytometry was performed on granulocytes, monocytes, and erythrocytes in PB using antibodies against GPI-anchored proteins (CD14, CD24, CD55, and CD59), fluorescein-labeled proaerolysin (FLAER) staining, and Navios cytometers; analysis was done using Kaluza software (both Beckman Coulter, Miami, FL). Results: PIGA mutations were newly identified in 67 patients (0.3%) undergoing targeted sequencing within the diagnostic workup of MN. 30 patients were excluded from further analysis as the gene panel had been requested for a MN associated with previously diagnosed PNH. From the remaining patients, PB for flow cytometry analysis could be obtained from 20 patients. Flow cytometry confirmed the presence of a PNH clone in 17 (85%) of these patients (median clone size: 41% for granulocytes, 54.5% for monocytes, and 12% for erythrocytes). In 3 patients (15%) with unexpected PIGA mutations, flow cytometry detected no PNH clone. The type of PIGA mutations differed significantly in those cases: Patients in whom a PNH clone was confirmed, showed protein-truncating frame-shift (41%) or nonsense (6%) mutations, splice site mutations (18%), or multiple mutations (35%) including at least one protein-truncating mutation at a median variant allele frequency (VAF) of 15.4% (range 2.0% to 50.1%). In contrast, patients without a PNH clone showed only singular missense mutations of PIGA with a VAF of 3.6% to 5.3% (Figure 1). Final diagnoses in patients with confirmed clones were sole PNH (n=9), or PNH clone associated with MDS (n=4), AA (n=3), and AML (n=1), and additional mutations in other genes were observed in 9 cases. While the initial clinical presentation included the differential diagnosis of PNH in some of the patients, flow cytometry was requested as a direct result of the PIGA mutation in 4 cases with an accompanying MN and in 3 patients without - the later showing a median latency of 6.5 years from the initial clinical presentation to the diagnosis. Con clusions: The inclusion of PIGA into a standardized targeted sequencing panel for MN helps to identify patients with PNH clones irrespective of the initial clinical suspicion but is not sufficient to rule out PNH. Protein-truncating PIGA mutations are highly specific for PNH clones whereas singular missense mutations may not necessarily effect GPI biosynthesis. Our data indicate that the incidental finding of a PIGA mutation in sequencing analysis shall entail flow cytometry of GPI-anchored proteins in PB. The potential clinical sequelae and the availability of specific treatment options such as complement inhibitors warrant the thorough exclusion of PNH in the diagnostic workup of suspected MN. Figure 1 Figure 1. Disclosures Hoermann: Novartis: Honoraria. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood-2021-153119
    RVK:
    XA 33000
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2021
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  • 6
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    AML and MDS Classification According to Who 2022 and International Consensus Classification: Do We Invent a Babylonian Confusion of Languages?
    American Society of Hematology ; 2022
    In:  Blood Vol. 140, No. Supplement 1 ( 2022-11-15), p. 555-556
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    In: Blood, American Society of Hematology, Vol. 140, No. Supplement 1 ( 2022-11-15), p. 555-556
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood-2022-162326
    RVK:
    XA 33000
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    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2022
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  • 7
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    Integrated Transcriptomic and Genomic Sequencing Identifies Prognostic Constellations of Driver Mutations in Acute Myeloid Leukemia and Myelodysplastic Syndromes
    American Society of Hematology ; 2019
    In:  Blood Vol. 134, No. Supplement_2 ( 2019-11-21), p. LBA-4-LBA-4
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    In: Blood, American Society of Hematology, Vol. 134, No. Supplement_2 ( 2019-11-21), p. LBA-4-LBA-4
    Abstract: CG Mullighan and T Haferlach: are co-senior authors Introduction: Recent genomic sequencing studies have advanced our understanding of the pathogenesis of myeloid malignancies, including acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS), and improved classification of specific subgroups. Unfortunately, these studies have mostly analyzed specific subtypes and/or used targeted DNA-sequencing, thus limiting discovery of novel mutational patterns and gene expression clusters. Here, we performed an integrated genome-wide mutational/transcriptomic analysis of a large cohort of adult AML and MDS samples to accurately define subtypes of diagnostic, prognostic and therapeutic relevance. Methods: We performed unbiased whole genome (WGS) and transcriptome sequencing (RNA-seq) of 1,304 adult individuals (598 AML and 706 MDS; Fig. 1A), incorporating analysis of somatic and presumed germline sequence mutations, chimeric fusions and structural complex variations. Transcriptomic gene expression data were processed by a rigorous bootstrap procedure to define gene expression subgroups in an unsupervised manner. Associations between genetic variants, gene expression groups and outcome were examined. Results: Genomic/transcriptome sequencing confirmed diagnosis according to WHO 2016 of AML with recurrent genetic abnormalities in 10.9% of cases. These cases had a distinct gene expression profile (Fig. 1A), good prognosis (Fig. 1B) and a combination of mutations in the following genes: KIT, ZBTB7A, ASXL2, RAD21, CSF3R and DNM2 in RUNX1-RUNXT1 leukemia; FLT3, DDX54, WT1 and CALR in PML-RARA promyelocytic leukemia; KIT and BCORL1 in CBFB-rearranged leukemia. In addition, 9% of cases showed rearrangements of KMT2A, with known (e.g. MLLT3) and non-canonical partners (e.g. ACACA, and NCBP1) and poor outcome. Although common targets of mutations have been previously described for myeloid malignancies, the heterogeneity and complexity of mutational patterns, their expression signature and outcome here described are novel. Gene expression analysis identified groups of AML and/or MDS lacking recurrent cytogenetic abnormalities (87%). The spectrum of the most frequently mutated genes ( 〉 10 cases) and associated gene expression subtypes is summarized in Figure 1A. TET2 (more frequent in MDS than AML, p=0.0011) and DNMT3A (more frequent in AML than MDS, p 〈 0.0001) were the most frequently mutated genes. Interestingly, mutations in these genes promoting clonal hematopoiesis were significantly enriched in the subgroup with NPM1 mutations. Overall, NPM1 mutations occurred in 27.4% of AML and 1% of MDS and were characterized by four expression signatures with different combination of cooperating mutations in cohesin and signaling genes and outcome (Fig. 1C, gene expression, GE, groups 2, 3, 7 and 8). Co-occurring NPM1 and FLT3 mutations conferred poorer outcome compared to only NPM1, in contrast co-occurring mutations with cohesin genes had better outcome (Fig. 1D). Additional mutations that significantly co-occurred with NPM1 were in PTPN11, IDH1/2, RAD21 and SMC1A. Three gene expression clusters accounted for additional 9% of cases with mutual exclusive mutations in RUNX1,TP53 and CEBPA and co-occurring with a combination of mutations in DNA methylation, splicing and signaling genes (Fig. 1E, GE groups 4, 5 and 6). Interestingly, RUNX1 mutations were significantly associated with SRSF2 mutations but not with SF3B1, showed high expression of MN1 and poor outcome (Fig. 1F). In contrast to the distinct, mutation-associated patterns of gene expression in AML samples, the gene expression profile of MDS was less variable despite diversity in patterns of mutation. MDS was enriched in mutations of SF3B1 (27.2%), mutually exclusive with SFRS2 (14.4%) and U2AF1 (5.5%); TP53 (13.7%) and RUNX1 (10.5%) and a combination of mutations in epigenetic regulators with outcome dependent on mutational pattern (Fig. 1A, G-H). Moreover, structural variations and/or missense mutations of MECOM accounted for 2% of cases. Conclusions: the integration of mutational and expression data from a large cohort of adult pan myeloid leukemia cases enabled the definition of subtypes and constellations of mutations and have prognostic significance that transcends prior gene panel-based classification schema. Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Baer:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Mullighan:Illumina: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: sponsored travel; Pfizer: Honoraria, Other: speaker, sponsored travel, Research Funding; AbbVie: Research Funding; Loxo Oncology: Research Funding; Amgen: Honoraria, Other: speaker, sponsored travel. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood-2019-132746
    RVK:
    XA 33000
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2019
    detail.hit.zdb_id: 1468538-3
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  • 8
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    How Whole Genome and Transcriptome Sequencing (WGTS) Can Contribute to Unsolved Cases in Hematology That Have Undergone Extensive Standard Diagnostic Workup: A Prospective Head-to-Head Study
    American Society of Hematology ; 2022
    In:  Blood Vol. 140, No. Supplement 1 ( 2022-11-15), p. 7824-7825
    Details
    In: Blood, American Society of Hematology, Vol. 140, No. Supplement 1 ( 2022-11-15), p. 7824-7825
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood-2022-166129
    RVK:
    XA 33000
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2022
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  • 9
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    Ultra-Deep Sequencing Leads to Earlier and More Sensitive Detection of the TKI Resistance Mutation p.T315I in CML
    American Society of Hematology ; 2014
    In:  Blood Vol. 124, No. 21 ( 2014-12-06), p. 4531-4531
    Details
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 4531-4531
    Abstract: Background: Chronic myeloid leukemia (CML) cells can acquire resistance to tyrosine kinase inhibitors (TKI) that in ~40% of cases is due to acquisition of mutations in the ABL1 kinase domain of the BCR-ABL1 transcript. The p.T315I (c.944C 〉 T) mutation (mut) mediates resistance to most BCR-ABL1 TKIs (Imatinib, Dasatinib, Nilotinib and Bosutinib), whereas sensitivity to ponatinib has been demonstrated. Patients with p.T315Imut show a rapid increase in malignant cell burden and can progress to blast crisis. An earlier detection of the p.T315Imut may allow TKI treatment intervention ahead of disease progression. However, the sensitivity of conventional Sanger sequencing for detection of mutations is not less than 10-20%. Aim: To study the dynamics of evolution and progression of the p.T315Imut using ultra-deep sequencing (UDS) in comparison with Sanger sequencing. Patients and Methods: We selected 18 CML patients with high p.T315Imut levels originally detected by Sanger sequencing for routine diagnostics. Subsequently, we backtracked prior blood samples of all patients for a mean period of eight months (2-15 months) before detection of p.T315Imut by Sanger sequencing, analyzing 3-7 time points per patient. Patients (4 female and 14 male) had a median age of 60 years (18-84 years) and received treatment as follows: only Imatinib (n=3), only Nilotinib (n=3), only Dasatinib (n=1), treated with two prior (n=6) or three prior TKIs (n=5) by the time of p.T315Imut detection by Sanger sequencing. For more sensitive mutation detection, we amplified the BCR-ABL1 fusion transcript and designed two sequencing amplicons (550 bp and 575 bp) for UDS with the XL+ Kit for extended read length (Roche/454, Branford, CT). A minimal read coverage of 1,000 per base was reached. Our backtracking study by UDS was performed on samples sent in at intervals of approximately 3 months. Results: To prove high sensitivity of UDS with the 454 XL+ protocol we performed dilution experiments for three sequence variants and replicated sequencing experiments with low level mutations. The detection limit was at 1-2% mutation level and thus is 10-fold better than the sensitivity reached by Sanger sequencing. At the time point of initial routine diagnosis of p.T315Imut the median mutation load was 87.5% (30-100%) by Sanger sequencing and very similar by UDS (median: 84%; range: 40-99%; R2=0.7). In 6/18 patients backtracking identified a sample with a low p.T315I mutation level of 〈 5% (1.9-13.6 months, median: 3.2 months) before a mutation load of 〉 10% (Sanger sequencing detection level) was reached. Thus, in 33.3% of all cases a small, early clone of CML with p.T315Imut was identified. At subsequent time points, all 6 patients experienced a strong increase of the p.T315Imut level ( 〉 50%), which represents the very fast expansion of the mutated clone. In a second subset of 10 patients, the p.T315Imut load was already 〉 30% when first detected by UDS. The median interval to the last p.T315I negative time point was 2.4 months (0.9-3.5) and no sample between the p.T315I negativity and high positivity was available. This subset confirms the fast outgrowth of the p.T315Imut positive clone. The p.T315Imut load had a median increase of 0.9% (0.2-3.1%) per day, when calculated as average increase from the last negative sample to the time point with maximum mutation load. The other 2 patients had high p.T315Imut levels ( 〉 40%) for our entire monitoring period. At the time of p.T315I detection by UDS, we observed eight patients with additional resistance mutations. The accumulation of mutations in one clone results in an extremely resistant CML. This was detected in one patient, where a p.T253H clone (Imatinib and Dasatinib resistant) gained the p.T315Imut. This clone expanded to 73% within 79 days. In contrast, we identified five cases with multiple CML clones carrying different mutations. However, the p.T315Imut clone was able to overgrow up to six other resistant clones. Conclusions: We showed: 1) the p.T315Imut rapidly increases upon occurrence, supporting the relevance of regular mutation monitoring in CML patients, when resistance to TKIs is suspected. 2) that small p.T315Imut clones in the 1-2% range can be sensitively detected by UDS in 33% of all samples if sampling intervals are within the 3 months range. 3) earlier detection of the p.T315Imut by UDS is a potentially valid method to allow a prompt change of TKIs before clonal expansion of the p.T315Imut cells. Disclosures Baer: MLL Munich Leukemia Laboratory: Employment; ARIAD Pharmaceuticals: Research Funding. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Mariathas:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership; ARIAD Pharmaceuticals: Research Funding.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood.V124.21.4531.4531
    RVK:
    XA 33000
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
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  • 10
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    Whole Genome Sequencing Identifies Microdeletions Affecting TET2 and RUNX1 with Clinical Impact in Myeloid Malignancies
    American Society of Hematology ; 2021
    In:  Blood Vol. 138, No. Supplement 1 ( 2021-11-05), p. 3672-3672
    Details
    In: Blood, American Society of Hematology, Vol. 138, No. Supplement 1 ( 2021-11-05), p. 3672-3672
    Abstract: Background: The current routine genetic work-up in hematological malignancies includes chromosome banding analysis (CBA) to detect complete or partial chromosomal deletions and fusions, and the identification of point mutations and small deletions or insertions by sequencing panels (max. length ~50 bp). Deletions of individual genes (e.g. IKZF1 in ALL) are only detected by specifically designed molecular tools. Therefore, those microdeletions might be overlooked by the current gold standard despite their clinical relevance. We established a bioinformatic pipeline to screen for microdeletions in whole genome sequencing (WGS) data of myeloid malignancies. Aim: (1) Screen for recurrent microdeletions in myeloid malignancies with a normal karyotype, and (2) characterize a patient specific profile of microdeletions in genes with known clinical and/or prognostic relevance. Patients and Methods: We analyzed 1356 cases (M/F: 778/578) of myeloid malignancies with a normal karyotype according to CBA (aCML: n=47; AML: n=251; CMML: n=165, mastocytosis: n=90; MDS: n=415, MDS/MPN-RS-T: n=69; MDS/MPN-U: n=42; MPN: n=250; PNH: n=27) using WGS. Median age was 71 [20-94] years. Amplification-free WGS was performed on the NovaSeq or HiSeq system with a median coverage of 103x (Illumina, San Diego, CA). Reads were aligned to the human reference genome (GRCh37, Ensembl annotation, Isaac aligner) and somatic copy number variant (CNV) discovery was performed with GATK (v 4.0.2.1), following best practice guidelines. Only gene overlapping CNV calls were considered for analysis (gene coordinates biomaRt (v 2.42.1), GRCh37 Ensembl). Results: On average, 38 genes per patient were partially or completely deleted and the size of the deletions ranged from 0.9 kb to 32 Mb (median 399 kb). The microdeletions affected a broad list of genes, but no gene was present in & gt;5% of myeloid malignancies. As technical validation, we used 36 B-ALL samples (normal karyotype) and identified the known deletions of IKZF1 (42%); PAX5 (25%) and CDKN2A/CDKN2B (22%) with expected incidences. We focused on a patient-by-patient analysis of genes (n=47) with known clinical relevance in myeloid malignancies. We identified deleted genes in 46 out of 1356 patients (3.4%). In aCML 13% of patients had one of the above-mentioned genes deleted (6/47), in mastocytosis only 1% (1/90). The most frequently deleted genes were TET2 (20/1356, 1.5%) and RUNX1 (9/1356, 0.7%). Other deletions also affected transcription factors (e.g. GATA2) or epigenetic regulators (e.g. DNMT3A, figure 1). No deletion of splicing factors, RAS genes or cohesion complex regulators was observed. We found only two deletions of kinases, which are predominantly affected by activating mutations (both FLT3). Instead, the deletions in 41 patients involved genes with a known loss-of-function mutation profile in myeloid malignancies. This corresponds to 89% (41/46) of patients with microdeletions or 3% (41/1356) of all analyzed patients with myeloid malignancies. Microdeletions are thus another genetic element that can lead to loss of gene activity. Deletions and mutations are either alternative genetic mechanisms or co-operate as double hits to affect the same gene. We found additional mutations present in 18 of the 46 patients with microdeletions (39%, figure 1). The majority of these (n=14) involved TET2. TET2 mutations had a median variant allele frequency of 82% [9-100%] indicative of a mutation on the non-deleted allele. For the remaining genes (incl. RUNX1), deletions are predominantly an alternative genetic mechanism to mutations. For validation of WGS results we applied interphase FISH and identified 6/9 RUNX1 deletions. The remaining three microdeletions were only detectable by WGS and too small to be identified by FISH. Conclusions: (1) WGS data unrevealed a plethora of microdeletions, which can be an alternative genetic mechanism to mutations, but are not detected with today's standard diagnostic tools. (2) In the light of increasingly personalized therapy and diagnostics, all genetic mechanisms should be considered, which impact the function of clinically relevant genes. (3) Bioinformatic pipelines for WGS as a potential diagnostic tool in the near future should address microdeletions in genes with relevance for patients' diagnosis, prognosis and hopefully targeted treatment. Figure 1 Figure 1. Disclosures Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: 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
    URL: Article
    DOI: 10.1182/blood-2021-150646
    RVK:
    XA 33000
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2021
    detail.hit.zdb_id: 1468538-3
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