MLL Munich Leukemia Laboratory Research Articles

Incidential Findings of Mutations in the PIGA Gene Are Highly Specific for the Presence of PNH Clones

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.Conclusions: 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. [Display omitted] DisclosuresHoermann: 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.

Automated Disease Classification Using Whole Genome Sequencing (WGS) and Whole Transcriptome Sequencing (WTS) Data with Transparent Artificial Intelligence (AI)

Background: Currently, hematologic neoplasms are diagnosed using a combination of methods, which require complex equipment and highly skilled clinical laboratory scientists and technicians - scarce resources.WGS and WTS could streamline this process and become a singular method. Interpretation of WGS and WTS data in a diagnostic setting is extremely challenging due to the breadth of data and its high-dimensional data types. AI will be mandatory to identify clinically meaningful genetic patterns and produce unbiased diagnosis.Aim: Compute leukemia diagnosis using AI methods with WGS and WTS data only, depicting relevant features for a decision and thus making its results comprehensible and transparent to humans.Methods: To train the model we used our cohort of 4,689 samples both with WGS (100x coverage, 2x151bp) and WTS (50 mio reads/sample, 2x101bp), along with our independent final routine diagnosis based on gold standard techniques (GST) and following WHO guidelines. Single nucleotide variants (SNV), structural variants (SV) and copy number alterations (CNA) from WGS data using a tumor w/o normal pipeline and gene fusions (GF) and gene expression (GE) from WTS were extracted. The cohort comprised of 30 different neoplasms and was severely imbalanced (n: 20 - 773).To test its performance another independent cohort which was not used during model creation (n=202, 22 entities) was selected.Results: We trained an ensemble of multi-class classifier using SageMaker (AWS, Seattle, WA) based on LGBM implementation of gradient boosted decision trees (Ke et al, 2017) in a one vs. rest architecture (1vRA). The model accuracy reached 85% overall on a 5-fold cross-validation (Fig 1a). Since neighboring disease types such as MGUS/MM, MDS-EB-2/AML/CMML are in some cases difficult to classify correctly using GST, we trained entity-specific classifiers operating independently. Rather than forcing a single predicted class to be predicted as overwhelmingly likely, this architecture accounts for ambiguous entities. In addition to reflecting biological similarity, the 1vRA resulted in improved probability calibration, so that cases with ambiguous leukemias are more easily identifiable by the distribution of predicted class probabilities and flagged for a human. Expected calibration error was only 3.8% for the 1vRA with entity-specific components, compared to 8.7% for a single LGBM model.Typically, AI methods are black boxes, making it hard for a medical professional to understand predictions, which results in low confidence and acceptance of such systems. Thus, we particularly focused on the transparency aspect of the model. We employed the SHAP library (Lundberg et al, 2017) to retrace the models output and gain insight into the features (i.e. which SNV, SV, GE etc.) predominantly driving classification results (e.g. LPL case Fig 1b). Fig. 1c illustrates the application of SHAP at the global cohort level for two individuals wrongly diagnosed with CML compared to CML correctly predicted cohort. By using a decision plot, we can observe which features are the most important contributors to the model's prediction. Fig 1c shows that predictions for CML are primarily driven by the BCR-ABL1 features, as expected.In our independent test cohort the following entities reached a very high concordance, such as AML (16/21) AUL (11/12), BCP-ALL (10/10), CML (13/13), HZL (8/8), MGUS (7/7), Multiple Myeloma (9/11), PNH (10/10), T-ALL (6/7). Other clear cut entities with correct high level predictions include BPDCN, FL, LPL, PPBL, NK-cell, HCL-variant and HGBL.In other entities such as T-NHL results were more heterogeneous, but this was also expressed in the probability scores given by the model. The first choice had a probability score of ~50%, exposing the correct diagnosis as the second likeliest one with ~40%. Test cohort included cases with mixed diagnostic characteristics, e.g. MDS/MPN-RS-T (4/11 correct, 4 predicted as MDS, and 3 as MPN).Conclusion: We present an AI tool to interpret WGS and WTS data aiming to predict the final diagnosis without any human input and high concordance to today's WHO classification. Due to the high data dimensionality of WGS and WTS data, an impossible feat for a human. The tool is exposed via a web application and visualizations make the automated decisions transparent and verifiable through humans paving a way for better adoption of WGS and WTS into a clinical routine setting. [Display omitted] DisclosuresKern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Copy-Neutral Loss-of-Heterozygosity Adds Diagnostic and Prognostic Information to the Molecular Profiling of Hematological Malignancies

Background: Copy-neutral loss-of-heterozygosity (CN-LOH) - not detectable by chromosome banding analysis - is gaining importance as a prognostic factor and can either cause the duplication of an activating mutation in an oncogene, the deletion of a tumor suppressor gene or the gain/loss of specific methylated regions. However, examination for possible CN-LOH in hematological diagnostics is at present not routinely performed and, hence, data regarding the occurrence of CN-LOH across different entities as well as the association of relevant genes is limited.Aim: (1) Frequency assessment of CN-LOH by target enrichment sequencing (TES) in a diagnostic setting, (2) evaluation of whole genome sequencing (WGS) data to estimate the prevalence of CN-LOH in a larger cohort, to pinpoint relevant genes for CN-LOHs with so far unknown associations, and to determine cross-entity variability.Patients and Methods: 1196 patients (507 female, 689 male, median age: 66 years), sent between 04/2021-07/2021 for diagnostic work-up, were analyzed by TES with a median coverage of 1765x for the gene panel and 52x for the CNV spike-in panel (IDT, Coralville, IA). Amplification-free WGS libraries of 3851 different patients were sequenced with a median coverage of 102x. Reads were aligned to the human reference genome (GRCh37, Ensembl annotation, Isaac aligner). Cnvkit (v 0.9.9) was used to call copy number variations (CNVs) and CN-LOH for TES and HadoopCNV (Yang et al. 2017) was used to call CN-LOH for WGS.Results: 1196 patients were analyzed by TES. For 10% of the patients at least one CN-LOH event was detected without any association to age or gender but a slightly higher incidence in myeloid compared to lymphoid neoplasms (10% vs 6%). In 14 patients, CN-LOH affected more than one chromosome arm. CN-LOH occurred most frequently in 4q (n = 15), 7q (n = 16), 9p (n = 25) and 11q (n = 10). As expected, 4q CN-LOH co-occurred with high variant allele frequencies (VAF) of TET2. Based on WGS data, 4q CN-LOH occurred predominately in AML (35%), CMML (22%), and MDS (20%). In rare cases, 4q CN-LOH was associated with FBXW7 variants in T-ALL. 7q CN-LOH occurred nearly exclusively in myeloid neoplasms (95%) and was associated with high VAFs in EZH2 in 69% of TES and 82% of WGS cases. CUX1 variants with high VAFs were detected in 80% (TES) and 45% (WGS) of the remaining cases, respectively. The well-known 9p CN-LOH led to JAK2V617F homozygosity in all myeloid neoplasms and occurred most often in MPNs. In T-ALL, regions of 9p CN-LOH harbored CDKN2A/B deletions. 11q CN-LOH occurred more often in myeloid than lymphoid neoplasms (79% vs 21%) and was associated with CBL variants in 61% and KMT2A-PTD in 19% of the cases. In contrast, ATM was the relevant gene in all lymphoid cases with 11q CN-LOH. CN-LOH in 11p was detected less frequently and only in 25% of cases an association with WT1 variants could be identified.Our WGS data confirmed the known associations between 1p CN-LOH and high allele burden in MPL, CSF3R and NRAS, 2p CN-LOH and DNMT3A variants, 13q CN-LOH and FLT3-ITD, the near exclusive occurrence of 16p CN-LOH in follicular lymphoma (FL, 98%) with high CREBBP-mutant allele burden , 17p CN-LOH and TP53 homozygosity, and the exclusive occurrence of 21q CN-LOH in AML and its association with RUNX1 mutations. Besides, 12q CN-LOH was associated with KMT2D in FL, with SH2B3 in MDS/MPN overlaps and in rare cases with KDM2B. For 17q CN-LOH the relevant gene was not unequivocally identifiable with high mutant allele variants in SRSF2, STAT5B, and NF1. 18q CN-LOH was a very rare event but consistently associated with a high VAF of MBD2, which presumably influences cell proliferation (Cheng et al. 2018). 19q CN-LOH was mostly (63%) associated with a high VAF of CEBPA variants, except for patients with hairy cell leukemia: in these cases nonsense mutations in CIC (VAF > 90%) were detected. CN-LOH in 22q was more common in myeloid malignancies (65% vs 35%) and associated with PRR14L mutations in the majority of myeloid cases (62%). Of note, this association occurred neither in AML samples nor in lymphoid neoplasms. No recurrent mutations were found for 6p and 14q CN-LOHs. For all other chromosomes, CN-LOH events were very rare.Conclusions: By using a CNV spike-in panel, TES adds additional diagnostic and prognostic information by enabling simultaneous detection of selected gene mutations and genome-wide CNVs, as well as CN-LOH, without increase in sequencing costs and turn-around times. [Display omitted] DisclosuresHaferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Automated Peripheral Blood Cell Differentiation Using Artificial Intelligence - a Study with More Than 10,000 Routine Samples in a Specialized Leukemia Laboratory

Background: Cytomorphology is the gold standard for quick assessment of peripheral blood (PB) and bone marrow samples in hematological neoplasms and is used to orchestrate specific diagnostics. Artificial Intelligence (AI) promises to provide an unbiased way of interrogating blood smear data as reproducibility varies across labs. This is a prospective clinical study (ClinicalTrials.gov Identifier: NCT04466059) conducted on our approach outlined at ASH 2020.Aim: Use an AI model to classify cell images to produce differential counts of PB smears side-by-side to routine diagnostics.Methods: We enrolled 10,082 patient samples which were sent to our lab between 01/2021 and 07/2021 for cytomorphology with a suspected hematologic neoplasm. Blood smears were differentiated by highly skilled technicians (median 5y in lab) and all were reviewed by hematologists. In parallel, all samples were scanned on a MetaSystems (Altlussheim, Germany) Metafer Scanning System (Zeiss (Oberkochen, Germany) Axio Imager.Z2 microscope, automatic slide feeder). Areas of interest were defined and leukocyte positions were flagged by pre-scan in 10x magnification followed by high resolution scan in 40x to generate cell images for analysis.We set up a supervised Machine Learning model based on ImageNet-pretrained Xception using Amazon Sagemaker (AS) and trained it on 8,425 carefully annotated color images to identify 21 predefined classes (including 1 garbage class). Overall accuracy of this model against hold-out-set (10%) was 96%. The algorithm consumes 144x144pixel cell images and produces probability scores (PS) for each class in every image.Results: For routine diagnostics in median 100 cells/sample (range 82 - 103) were differentiated manually, overall 988,130. The automated process gathered 500 cell images/sample (range 101 - 500), overall 4,937,389. Average capture times for 500 cells: 4:37 min. Cropped images were uploaded to a cloud storage and exposed to an AS endpoint to initiate classification and the computation of a PS for each of the predefined 21 classes in the model.For the study we only considered images with a probability of at least 90% (n=3,781,670/4,937,389) and excluded normoblasts, smudge cells and images identified as garbage (together n=2,120,258). Final diagnosis included: no lymphoma detectable (2,186), MDS (1,152), AML (369), in these 11 APL, MPN (658), CLL (558), other mature B-cell neoplasms (377), CML (326), multiple myeloma (155), but also rare entities such as hairy cell leukemia variant (2) or PPBL (3).Comparing the benign normal cells in peripheral blood we identified (all values normalized) segmented neutrophils (manual (M): 516,648=52% vs AI: 882,538=53%), eosinophils (M: 24,860=2.52% vs. AI: 55,699=3.36%), basophils (M: 7159=0,72% vs. AI: 11,957=0,72%), monocytes (M: 74,113=7.5% vs. AI: 110,126=6.64%), lymphocytes (M: 313,518=31.7% vs. AI: 399,249=24%).Pathogenic blasts were detected in 16,048 (0.97%) images by AI (M: 16,290=1.65%). In routine diagnostics 536 cases with blast cells, including “questionable blasts” were identified. The AI identified 493 (91%) of these cases. At least one atypical/malignant lymphocyte was found in 2,323 samples manually, out of which the AI identified 2,279 (98%). In few cases manual differentiation relies on the number of pathogenic cells from an immunophenotyping analysis, which the AI does not had.During the course of the study by chance we identified at least 3 instances, were the AI detected pathogenic cells (blasts, atypical promyelocytes (APL) or bilobulated promyelocytes (APL-v)) which were initially missed manually (in some case WBC below .5 G/l) or flagged during subsequent immunophenotyping/molecular genetic analysis. Upon manually revisiting the smear, we could verify the presence of the AI-anticipated cells, revealing the higher sensitivity of the 5 time increase in cells/sample investigated by AI and power of algorithms.Conclusion: We present data of a prospective, blinded clinical study comparing blood smear analysis between humans and AI head-to-head. The concordance is extremely high with 95% for pathogenic cases. Misclassified cells are used for retraining to continuously improve the model and benefit from large datasets even for rare cell types. The model's cloud based implementation makes it easy to connect scanning devices for automated, unbiased classification. DisclosuresHaferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

TP53 Mutations, Deletions, and CN-LOH: Comparison of TP53 single Hit and Double Hit Events and Their Impact on Prognosis in Hematological Malignancies

Background: TP53 is altered in ~50% of human cancers. Alterations mainly include mutations and/or deletions, but also copy-neutral loss of heterozygosity (CN-LOH) was reported. Frequently, both TP53 alleles are altered (by mutation + deletion, mutation + CN-LOH or ≥2 mutations), leading to a “double hit” event.Aim: Analysis of TP53 aberrations using WGS in 4646 cases with 29 different hematological malignancies, comparing (1) the frequencies of TP53 alterations, (2) occurrence of single hit vs. double hit, (3) correlation with complex karyotype and (4) impact on survival.Methods: Whole-genome sequencing (WGS) was performed for all 4,646 patients (median coverage 100x). 151bp paired-end reads were generated on NovaSeq 6000 and HiSeqX machines (Illumina, San Diego, CA). As no sample specific normal tissue was available, a so-called Tumor/Unmatched normal (TUN) workflow was used to reduce technical artefacts and germline calls. All reported p-values are two-sided and were considered significant at p<0.05.Results: In the total cohort of 4,646 cases, in 582 (13%) at least one alteration (alt) involving TP53 was detected (comprising mutations (mut), deletions (del) and CN-LOH (LOH); Fig 1A,B). Cases were categorized as follows: cases with (1) 1 TP53 mut only (n=166), (2) del only (n=100), (3) LOH only (n=15), constituting the single hit events. Further, (4) cases with mut+del (might include 1 or more mut, n=211), cases with mut+LOH (≥1 mut, n=41), cases with ≥2 mut only (without del or LOH, n=49), resulting in double hit events (Fig 1B). Regarding the respective entities, high frequencies of TP53 alt were mainly detected in lymphoid malignancies (e.g. HGBL, MPAL, vHZL, MZL, MCL), whereas they were infrequent or absent in many myeloid malignancies (e.g. aCML, MPN, CMML, CML, MLN_eo; Fig 1A). For further analysis, only entities in which >10 cases showed TP53 alt events were used. Comparison of single hit vs. double hit revealed that T-NHL, MM, MPN and MDS predominantly showed a single hit, whereas the double hit was frequent in MPAL, MZL, MDS/MPN-U, CLL and MCL cases (Fig 1A). However, the type of double hit differed between myeloid and lymphoid malignancies, as myeloid neoplasms showed a high frequency of cases with ≥2 mut only, whereas in many lymphoid malignancies the double hit was predominantly generated by mut+del (Fig 1A,C). All TP53-associated events (mut, del, LOH and the respective combinations) were found to be associated with a complex karyotype in the total cohort (LOH: 14% complex karyotype in cases without TP53 alt vs. 59% in cases with TP53 alt, p<0.001). This association was also detected for most of the selected entities (exceptions: MZL, T-NHL). Regarding overall survival (OS), in the total cohort, all events involving TP53 impact on OS (TP53 alt: 22 months vs. 84 months, p<0.001; TP53 mut: 20 vs. 82 months, p<0.001; TP53 del: 20 vs. 79 months, p<0.001; TP53 LOH: 20 vs. 75 months, p<0.001). Moreover, although the single hit already impacts on OS, the double hit leads to an even inferior outcome (no hit vs. single hit vs. double hit: 84 vs. 39 vs. 14 months, p<0.001). In the selected entities, an influence of TP53 alt on OS was detected for all malignancies except HGBL, MZL and T-NHL, for which also the presence of a double hit did not show an effect on OS. For the majority of the other entities, the double hit leads to a shorter OS than the single hit (as observed for the total cohort), with the exceptions of MCL and MPAL: in these entities, the single hit did not impact on OS, but only a double hit is associated with inferior outcome.Conclusions: (1) Frequency of TP53 alterations and of double hit vs. single hit differs markedly between entities. (2) The kind of TP53 complexity differs between both lineages (double hit in myeloid neoplasms: often ≥2 mut only; in lymphoid malignancies: predominantly mut+del). (3) In 7% (41/582) of cases with TP53 alt, CN-LOH transforms a single hit into a double hit. (4) In the total cohort and in the majority of selected entities (except MZL and T-NHL), TP53 alt are associated with complex karyotype. (5) In the total cohort, all events involving TP53 impact on OS; cases with double hit show an inferior outcome compared to single hit. (6) Regarding OS, the selected entities can be divided into three categories: (i) no influence of TP53 alt (HGBL, MZL, T-NHL); (ii) double hit required for impact on OS (MCL, MPAL); (iii) influence of both single hit and double hit with inferior outcome of double hit (all other). [Display omitted] DisclosuresKern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Der(1;7)(q10;p10) Presents with a Unique Genetic Profile and Frequent ETNK1 Mutations in Myeloid Neoplasms

BackgroundDer(1;7)(q10;p10) (der(1;7) is an unbalanced translocation recurrently found in myeloid neoplasms, particularly in myelodysplastic syndromes (MDS) and related disorders. Caused by a recombination between two homologous alphoid sequencing D1Z7 and D7Z1 on chromosomes 1 and 7, respectively, it results in monosomy 7q and trisomy 1q, which is implicated in the pathogenesis of der(1;7)-positive myeloid neoplasms. Previous studies reported frequent co-occurrence of +8 and del(20q), as well as RUNX1 mutations, the genetic and clinical characteristics of this abnormality has not fully been elucidated.MethodsIn this study, we enrolled a total of 153 cases myeloid neoplasms positive for der(1;7) from Japanese and German cohorts, in which co-occurring genetic lesions were analyzed using whole exome and/or targeted-capture sequencing. An additional 3,223 MDS and related neoplasm cases were also analyzed using targeted-capture sequencing to identify der(1;7)-specific genomic features.ResultsEthnicity was evaluated comparing the frequency of der(1;7) in 944 German MDS cases and 763 Japanese MDS cases. Der(1;7) cases were observed at a higher frequency in Japanese MDS cohort compared to German MDS cohort (73/763 cases versus 4/944 cases, p < 0.00001). Der(1;7) cases showed a strong male predominance (86.3%) (p<0.001). Of 153 myeloid neoplasm patients harboring der(1;7), 114 were diagnosed with MDS, 28 with AML, 5 with MDS-MPN and 1 with MPN. Targeted-capture sequencing revealed mutations in common myeloid drivers (n=61) in 96% of der(1;7) cases. The most frequently mutated gene was RUNX1 with 46%, followed by ETNK1 (24.5%) and EZH2 (24.5%). Of interest, ETNK1 mutation was identified as the most unique to der(1;7) when compared to myeloid neoplasm cases without der(1;7) (n=3,066) [odds ratio (OR)=15.06], followed by ETV6 (OR=9.35) and EZH2 (OR=6.52). To further examine the uniqueness of this mutation profile, the mutational profile of der(1;7) was compared to those myeloid neoplasm cases harboring amp(1q) (n=52) and monosomy 7 (n=105). Highly frequent ETV6 and ETNK1 mutations were highly unique to der(1;7) cases when compared to amp(1q) cases (OR=3.72, OR=2.57, respectively). BCOR and ETNK1 mutations were highly unique to der(1;7) cases when compared to monosomy 7 cases (OR=35.88, OR=4.29, respectively). Both amp(1q) and monosomy 7 cases showed a higher mutation rate in TP53 compared to der(1;7) cases (49.1% and 51%, respectively, vs 3.5 %) . From these mutational characteristics, ETNK1 was identified as being the most unique to der(1;7) when compared to amp(1q), monosomy 7 and other myeloid neoplasm cases. ETNK1-mutated der(1;7) cases were featured with eosinophilia (p < 0.0005), a lack of RAS pathway mutations and trisomy 8 when compared to ETNK1-wild type der(1;7) cases.Survival analysis was conducted to elucidate the difference in survival in der(1;7) cases (n=65) versus myeloid neoplasm cases (n=2066). Der(1;7)-harboring myeloid neoplasm cases had a median overall survival of 6.8 months (95% CI, 3.5 to 11.9) and non-der(1;7) harboring myeloid neoplasm cases were 11.8 months (95% CI, 10.5 to 12.6). Thus, der(1;7)-harboring myeloid neoplasm cases had poorer prognosis (p<0.001).ConclusionIn conclusion, der(1;7) is an unbalanced translocation that occurs predominantly in males and is seen more frequently in Japanese than Caucasian populations. Der(1;7) cases present with a mutational profile that is distinct from other myeloid neoplasm cases such as those with amp(1q) and monosomy7/del(7q), showing frequency of ETNK1 mutations. DisclosuresNannya: Otsuka Pharmaceutical Co., Ltd.: Consultancy, Speakers Bureau; Astellas: Speakers Bureau. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Atsuta: Astellas Pharma Inc.: Speakers Bureau; Mochida Pharmaceutical Co., Ltd.: Speakers Bureau; AbbVie GK: Speakers Bureau; Kyowa Kirin Co., Ltd: Honoraria; Meiji Seika Pharma Co, Ltd.: Honoraria. Handa: Ono: Honoraria; BMS: Honoraria; Janssen: Honoraria; Daiichi Sankyo: Research Funding; Celgene: Honoraria, Research Funding; Chugai: Research Funding; Kyowa Kirin: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; Abbvie: Honoraria; MSD: Research Funding; Shionogi: Research Funding. Ohyashiki: Novartis Pharma: Other: chief clinical trial; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Ogawa: Otsuka Pharmaceutical Co., Ltd.: Research Funding; Eisai Co., Ltd.: Research Funding; Kan Research Laboratory, Inc.: Consultancy, Research Funding; Dainippon-Sumitomo Pharmaceutical, Inc.: Research Funding; ChordiaTherapeutics, Inc.: Consultancy, Research Funding; Ashahi Genomics: Current holder of individual stocks in a privately-held company.

FISH and WGS in Newly Diagnosed and Relapsed/Refractory Multiple Myeloma - WGS Will Affect Future Treatment Decisions

Background:FISH is the gold standard for genetic characterization and risk stratification of multiple myeloma (MM) at diagnosis. Retrospective studies have shown the potential of whole genome sequencing (WGS) to gain new insights into MM genetics. With respect to clinical application, we prospectively performed FISH and WGS in parallel in patients (pts) with newly diagnosed MM (NDMM) and relapsed/refractory MM (RRMM) to evaluate advantages and limitations.Patients and methods:From bone marrow (BM) samples of 280 MM pts CD138+ cells were enriched by MACS. In 100 pts (30 NDMM, 70 RRMM) FISH (del(1p32), del(13q), del(17p), t(4;14), t(11;14), t(14;16), t(14;20), t(6;14), 1q gain, MYC rearrangement (MYCr) and trisomies 3, 5, 9) and WGS were performed. WGS: 150bp paired-end sequences were generated on NovaSeq 6000 (Illumina); median coverage 104x. Single nucleotide variants (SNV) were called with Strelka software, copy number variations (CNV) with GATK4 and structural variants (SV) with Manta. Technical artefacts and germline calls were reduced by a tumor/unmatched normal workflow with genomic DNA from a mix of anonymous donors. Variants with global population frequency >0.5% (gnomAD) were excluded.Results:In 100/280 (36%) MM pts WGS could be performed in parallel to FISH in this cohort with a low median BM plasma cell infiltration (PCI) of 6% (range 1-95%). Likelihood of DNA yields sufficient for WGS increased with higher PCI (23% (1-95%) vs 3% (1-50%) in 100 pts with WGS vs 180 pts without sufficient DNA).Comparison of FISH and WGS results for 100 pts showed 100% concordance for recurrent SV (table 1): t(11;14) in 29%, t(4;14) in 17% and t(14;16) in 5%. No pts harbored a t(14;20) but in one case FISH indicated another alteration of MAFB and WGS revealed MAFB rearranged to FAM46C resulting in an elevated MAFB expression.Due to heterogeneous breakpoints FISH is less reliable for the detection of MYCr than WGS (24/42 (57%) detected by FISH vs 40/42 (95%) by WGS). Partners of MYC included IGH (n=10), FAM46C (n=4), IGK (n=3), IGL (n=3), FOXO3 (n=2) and IRF4 (n=1). Rare IGH rearrangements with partner genes such as IRF1, IRF4, ZBTB38 and ZFP36L1 were observed in 11 pts all identified by WGS, but only 8 (73%) were detected by FISH.Total frequencies of CNV were del(13q) in 50%, del(17p) in 29%, del(1p32) in 22%, 1q gains in 61% and hyperdiploidy in 36%. WGS detected 95% of CNV (details table 1). CNV that were missed by WGS included one del(17p) and were all identified in small subclones with a median clone size of 10% (6-25%) in FISH.Tetraploid MM, potentially harboring adverse prognostic implication, was identified in 11 pts by FISH but not in WGS.However, in 97 pts WGS depicted additional SV and CNV compared to FISH. This includes aberrations of prognostic significance such as a TP53 deletion that was located adjacent to a 451kb gain on 17p. The FISH probe encompassing TP53 confirmed the 17p gain but masked the TP53 deletion. Also 1p deletions, which have been described to be a negative prognostic factor, were found more frequently by WGS with 24 pts harboring a del(1p) other than del(1p32) (locus evaluated by FISH) which was detected in 22 pts by both methods. Furthermore, as WGS cannot only detect SV and CNV but also SNV, WGS allows screening for potentially clinically exploitable drug targets, e.g. BRAF V600E mutation (5 pts in our cohort) or resistance associated mutations such as in cereblon for immunomodulatory compounds (CRBN mutation in 3 pts) as well as biallelic events in TP53 (del(17p)+TP53 mutation), detected in 15 of our pts suggesting worst prognosis for affected pts. WGS in RRMM can shed light on the genetic background of relapses. Of note, homozygous del(16p) inducing loss of the treatment target were first described using our dataset in two patients with acquired resistance toward anti-BCMA CAR T cells or bispecific antibody therapy.Conclusions:FISH is the preferred approach in pts with BM PCI <10% and for the detection of clones <15% or variant ploidy levels. WGS is superior to FISH regarding the identification of biallelic events and rearrangements with heterogeneous breakpoints and rare partners. But even more, WGS can address various questions in follow up in terms of increase of complexity, target specific therapy, therapy failure and relapse. Thus, WGS allows a holistic insight into the genetics of MM and provides crucial information for clinical decision making. [Display omitted] DisclosuresKern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Einsele: Janssen, Celgene/BMS, Amgen, GSK, Sanofi: Consultancy, Honoraria, Research Funding. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Mutant TP53 prevents Telomere Shortening in Acute Myeloid Leukemia

BackgroundHigh telomerase activity represents a critical feature of hematopoietic stem cells. Excessive shortening of telomere length (TL) due to replicative stress may be - in analogy to many solid tumors - a hallmark of myeloid neoplasia (MN). Also, telomeric footprints in leukemic genomes may vary between various subtypes corresponding to the differentiation arrest at various stages of hematopoietic ontogeny or specific molecular defects. Critical TL shortening has been associated with genomic instability and accelerated acquisition of genomic lesions leading to a more aggressive phenotype. These processes have not been systematically studied in MN, especially AML.AimBy taking advantage of next-generation sequencing to assay both molecular features and TL within large cohorts of patients, we tested the hypothesis that TL shortening is excessive in highly proliferative MN, but that distinct invariant differences characterize genetic subtypes.MethodsOur cohort included AML (N=734), MDS (N=701), healthy controls (HC) (N=11) and PNH (N=102) serving as clonal non-malignant controls. All patients were diagnosed according to WHO standards before being subjected to transcriptome (WTS) and genome (WGS, 100x) sequencing. To retrieve TL characteristics and telomere repeat heterogeneity from WGS data, we used TelomereHunter (TH). In parallel, we performed C-Circle assays. Patients were annotated for clinical features and analyzed for genetic/transcriptomic patterns.ResultsFor a subset of patients for whom corresponding benign lymphocyte DNA was available a significant TL shortening in blasts vs control lymphocytes (A; P=.0023) was detected. While age correlation was established in controls, despite a trend, in MN age did not significantly affect TL (B) and thus subsequent comparisons were not adjusted for age. Next, we studied a cohort of patients with AML, MDS, PNH and HC and found that TL shortening was an overarching finding in AML, MDS and PNH as compared to HC (C). Since no matched DNA was available as reference, we examined the distribution of TL across different age cohorts, AML patients divided according to age cohorts harbored TL in a similar range (D, P=.057). Classic morphologic (E) or cytogenetic subtypes AML exhibited no difference. Similarly, no differences were found between high and low risk MDS patients (not shown). The variability of TL ranges suggested that there may be molecular factors which affect individual TL. When we compared TL grouped according to frequent mutations, only TP53 mutations were associated with longer TL (F, P<.0001). A significant positive correlation (G, P=.021) between TL and TP53 clonal burden was found; samples with the longest vs shortest TL showed significantly higher TP53 VAF (H, P=.0229). In analogy, the presence of multiple TP53 mutations (putative biallelic inactivation) showed longer TL than single hits but no association was found between the nature of mutations and TL (I, J, K). Availability of WTS data allowed us to assess the telomerase activity using the EXTEND score (ES) which has been shown to assess telomerase activity. Indeed, the ES was correlated with TL (L) and TP53 mutant status was associated with a higher ES compared to WT samples (M, P<.0001). Similarly, because of the compensatory upregulation of TP53 in mutant cases, we have also found that TP53 mRNA levels correlated with ES (N P<.0001). Another explanation of TL increase could be the occurrence of alternative lengthening (ALT). TH software allows for estimation of the abundance of specific telomeric repeats. Singleton analysis showed that increase in telomere repeats variants (TTTGGG, O, P=.003) was related to mutations in TP53 arguing against the involvement of ALT. The final confirmation that TL extension was not due to ALT was provided by C-Circle assays. When C-Circle assays were performed for samples with a high/low TL and mutant/WT TP53, none of the subgroups was identified as ALT + (P).ConclusionWe stipulate that TL measurements using NGS will be helpful to investigate pathophysiological features associated with TL shortening. Availability of therapies targeting the telomere machinery (Imetelstat) may offer an opportunity for personalized therapy beyond MPN, its current indication. It remains to be tested whether long TL associated with TP53 mutations can serve as marker of sensitivity or resistance to these agents. [Display omitted] DisclosuresHaferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Maciejewski: Regeneron: Consultancy; Bristol Myers Squibb/Celgene: Consultancy; Alexion: Consultancy; Novartis: Consultancy.

Whole Genome Sequencing Identifies Microdeletions Affecting TET2 and RUNX1 with Clinical Impact in Myeloid Malignancies

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 >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. [Display omitted] DisclosuresKern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Automated Comprehensive Diagnostics of Hematologic Neoplasms By Artificial Intelligence Models Using Flow Cytometric Raw Matrix Data

Background: Artificial intelligence (AI) has steadily been entering the field supporting diagnostic workup of hematological neoplasms. Its application in flow cytometry (FC) so far mostly included visualization steps with the potential disadvantage of data reduction.Aim: To implement AI models based on raw matrix data for diagnosing main entities of hematologic neoplasms by FC.Methods: For examination of acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), myelodysplastic syndromes (MDS), multiple myeloma (MM) and mature T- and B-cell neoplasms (T-NHL, B-NHL), six machine learning (ML) models were trained on the respective dataset consisting of uniformly analyzed samples (Navios and Cytoflex cytometers, Kaluza analysis software, Beckman Coulter, Miami, FL) resulting in “.fcs” or “.lmd” files, after being classified and diagnosed by human experts. In total 36,662 cases were included, in detail 3,961 for AML model (3,120 AML, 841 no AML), 2,931 for T-NHL (204 T-NHL, 40 NK-cell neoplasm [NK], 2,687 no NHL), 766 for ALL (364 c-ALL/Pre-B-ALL, 95 Pro-B-ALL, 55 cortical T-ALL, 34 Pre-T-ALL, 11 Pro-T-ALL, 3 mature T-ALL, 15 ETP-ALL, 189 no ALL), 7,503 for MM (1,297 MM, 1,261 consistent with MM (<10% plasma cells by FC), 3,613 consistent with monoclonal gammopathy of undetermined significance (MGUS), 1,332 no MM/MGUS), 9,664 for B-NHL (440 hairy cell leukemia (HCL), 3,771 chronic lymphocytic leukemia (CLL), 3,062 CD5-negative NHL, 1,318 CD5-positive NHL, 1,073 no NHL) and 11,837 for MDS (5,206 consistent with MDS, 6,631 no MDS). For each model, feature engineering (FE) techniques were applied. These included division of values by their maximal values, multiplication by 1024, standardization, arcsinh transformation and one- (for all models) or two- (for T-NHL and ALL) dimensional distribution of marker values using empirical cumulative distribution functions (cdfs), with the number of bins set to two for two-dimensional histograms and between 16 and 128 for one-dimensional histograms optimized for each model. Further expert-based features were applied (for T-NHL, ALL, MM, MDS) including setting positive/negative thresholds on marker values, focusing on cell populations of interest by applying clustering techniques, considering percentages of certain cell types and calculating features to capture their specific properties (e.g. distribution of markers of the subpopulations). For MM, B-NHL and MDS we also calculated covariance between key markers. Taken together, 345 features were applied for AML, 772 features for T-NHL, 339 for ALL, 1,800 for MM, 3,275 for MDS and 3,145 for B-NHL. Following ML models were used: XGBoost, weighted SVC and LinearSVC, hierarchical model (four XGBoost with SMOTE models), AutoGluon (using weighted L2 ensemble of XGBoost, LightGBMXT and CatBoost). For MDS an approach similar to manual gating strategies was implemented dividing cells into five partitions combining predictions for each partition to a final result. Model performance was assessed with stratified five-fold cross validation (training/test set 80/20% of data) repeated 10 times. Test recall (R), precision (P) and prediction probabilities (PP) were recorded.Results: Application of the ML models (see figure 1) detected AML vs no AML with average R (aR) of 99.8% and average P (aP) of 99.9% when considering cases with PP ≥0.9 covering 82% of all cases analyzed for AML. For T-NHL we saw aR of 87% and aP 86.7% for detection of NK, T-NHL and no NHL, respectively. In general PP for NK were low and thus prohibited application of a high PP threshold which would have excluded many cases. With a PP threshold of 0.9 (82% of cases) ALL model resulted in prediction of classes Pro-B-ALL, T-ALL non-cortical, c-ALL, cortical T-ALL and no ALL with aR 91.7% and aP 92.5%. MM model separated consistent with MGUS from consistent with MM and no MM (PP=0.9, 66% of cases) with aR 97.7 % and aP 93.5 %. For MDS aR was 85.6% and aP 84.7% with PP=0.9 (74% of samples). Applying PP of 0.9 (51% of cases) B-NHL model classified CD5 negative, CD5 positive, CLL, HCL and no NHL with aR 84.6% and aP 91.5%.Conclusions: Training AI models for FC using raw matrix data is feasible and yields striking R and P values for various models when restricting to cases with high PP. Besides further improving all models future work will focus on identification of additional sub-entities and application of transfer learning to achieve universal applicability to any FC data. [Display omitted] DisclosuresHaferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership.

A Completely Digital Workflow for Differentials in Bone Marrow Cytomorphology Supported By Machine Learning Provides Promising Results in Object Detection

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. [Display omitted] DisclosuresHaferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Whole Genome Sequencing Identifies Non-KIT Mutations and Cytogenetic Aberrations in Systemic Mastocytosis but Has Limited Sensitivity for Detection of KIT D816V

▪Background: Systemic mastocytosis (SM) is a hematologic neoplasm characterized by the infiltration of clonal mast cells in the bone marrow or other extra-cutaneous organs. The clinical course varies between advanced and non-advanced (indolent and smoldering SM) forms of SM. The vast majority of patients harbor the activating D816V mutation in the KIT tyrosine kinase. Additional somatic mutations in other genes have been recognized as risk factors in SM. Cytogenetic aberrations are rarely found in SM but have been associated with advanced disease. Whole genome sequencing (WGS) and whole transcriptome sequencing (WTS) have been described as an alternative to cytogenetics and targeted molecular genetic analysis in myeloid cancers.Aim: To assess the ability of WGS/WTS to detect cytogenetic aberrations and recurrent somatic mutations in SM.Methods: 120 patients (51 female, 69 male) diagnosed with SM were analyzed with WGS/WTS and results were compared with orthogonal data of KIT D816V PCR, targeted sequencing, and cytogenetics. 47 patients (39%) were diagnosed with advanced SM (1 mast cell leukemia, 3 aggressive SM, 43 SM with associated hematologic neoplasm). For WGS, 2x151bp paired-end reads were generated on NovaSeq 6000 and HiSeqX machines (Illumina, San Diego, CA). BaseSpace's Tumor/Normal app v3 was used to call variants with Strelka Somatic Variant Caller v2.4.7 and structural variants (aberrations with >50bp in size) with Manta (v0.28.0). Genomic DNA from a mixture of multiple anonymous donors (Promega, Fitchburg, WI, USA) was used as normal. For WTS, 2x101 bp paired-end reads were produced with a median of 50 mio. reads per sample, aligned with STAR v2.5.0, and variants were called using Isaac Variant Caller v2.3.13.Results: WGS/WTS detected cytogenetic aberrations in 21% of patients: 2 patients displayed a complex aberrant karyotype, 3 balanced structural aberrations, 16 copy number alterations, and 6 copy number neutral losses of heterozygosity. Aberrations detected by chromosome banding analysis were also found by WGS in all but three patients (small clones with aberrations present in ≤20% of metaphases and <10% of interphase nuclei as determined by FISH). In contrast, WGS/WTS detected additional aberrations in 16 patients. The frequency of chromosomal aberrations detected by WGS/WTS was higher in advanced compared to non-advanced SM (34% vs. 12%, p<0.05). KIT D816V was detected by PCR in 98%, by WGS in 21% and by WTS in 46% of patients. The detection rate by WGS was significantly higher in advanced (36%) compared to non-advanced SM (12%, p<0.05) while no difference was observed for WTS (45% vs. 47%). Somatic mutations outside of KIT were analyzed within a subset of 121 genes recurrently mutated in hematologic neoplasms. 46% of patients showed non-KIT mutations with a median of 2 mutations per patient. Both frequency of non-KIT mutations as well as the median number of mutations per patient was higher in advanced (83%; n=3) compared to non-advanced SM (22%, n=1, p<0.05). Finally, we analyzed the impact of genetic aberrations on survival in our SM cohort. Patients were grouped according to the presence of chromosomal aberrations and gene mutations (non-KIT) as assessed by WGS/WTS. SM patients with both types of aberrations (n=16), one type of aberration (n=47; gene mutations only n=38; chromosomal aberrations only n=8), or no aberration but KIT D816V (n=57) showed significant differences in overall survival (p<0.05, Figure 1).Conclusions: WGS/WTS has limited sensitivity for detection of KIT D816V in SM. This finding can be explained by the low KIT D816V mutation burden typically found in bone marrow aspirates of SM patients. In line, we observed a slightly higher detection rate in advanced SM and in RNA-based WTS analysis. As WGS/WTS will be applied for the diagnostic workup of myeloid malignancies in the future and SM associated with other hematologic neoplasms may be overlooked if not specifically investigated, additional PCR-based techniques are still mandatory to rule out KIT D816V as a diagnostic criterion for SM. In contrast, WGS/WTS detects both chromosomal aberrations and additional gene mutations in patients with SM and can be used as an alternative to cytogenetics and targeted sequencing for risk assessment. In particular, the absence of genetic aberrations in WGS/WTS identifies SM patients with indolent course of the disease and favorable prognosis. [Display omitted] DisclosuresHoermann: Novartis: Honoraria. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Benchmarking of Whole Genome Sequencing (WGS) and Whole Transcriptome Sequencing (WTS) As Diagnostic Tools for the Genetic Characterization of Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) in Adults

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 > 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 > 15% (n = 647) identified by TPS encompassing especially all mutations in genes relevant for WHO diagnosis and prognostication. 26/171 mutations with a VAF < 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. [Display omitted] DisclosuresHaferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Epigenetic Enzyme Mutations in Myeloid Malignancies Are Selected By Chromatin-Remodeling Requirements That Vary By Lineage- and Maturation-Stage

Introduction/Methods: Enzymes that modify histone H3 at lysine 27 (H3K27) to thereby regulate gene transcription (epigenetic enzymes) are recurrently inactivated by deletion and/or mutation in myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPN) and acute myeloid leukemias (AML). Frustrating understanding of mechanisms is that writers and erasers of the same modification, e.g., methyltransferases that create, and demethylases that remove, H3K27 trimethylation (H3K27me3), a repression ('off') mark, are recurrently inactivated. Moreover, acetyltransferases that write H3K27 acetylation (H3K27ac), an 'on' mark mutually exclusive with H3K27me3, are also recurrently inactivated. One clue to underlying mechanisms is that MDS/MPN/AML present with diverse lineage and maturation phenotypes - perhaps these emerge from, or select for, the diverse epigenetic enzyme mutations. We therefore identified genes upregulated with specific myeloid lineage-commitment, maturation and function fates (~500 genes each) and then examined distributions of H3K27me3 and H3K27ac at these gene-loci in: (i) embryonic stem cells (ESC); (ii) hematopoietic stem and progenitor cells (HSPC); (iii) mature myeloid cells (monocyte [mono], pro-erythroblast, megakaryocyte [MK]); and (iv) AML cells.Results: Terminal-myeloid programs underwent substantial remodeling to gain H3K27ac ‘on’ mark from ESC/HSPC to mature myeloid (Fig.1A). Providing a mechanism for this, the H3K27 acetyltransferases EP300 and CREBBP were recruited into the RUNX1/SPI1 myeloid-lineage master transcription factor (MTF) hub by cooperation between their transcription activating domains. Mutated/translocated RUNX1, or mutated-NPM1 that cytoplasmically dislocated SPI1, disrupted this cooperation, and reverted hub content to default recruitment of histone deacetylases (HDAC) instead. Demonstrating cause-effect, inhibiting these HDAC renewed AML cell maturation to terminal lineage-fates. Meanwhile, MYC-target (proliferation) genes have high baseline H3K27ac in ESC and HSPC and do not require major remodeling during ontogeny (Fig.1A). An H3K27ac remodeling requirement for lineage-maturation but not proliferation/housekeeping explains selection pressure for inactivating mutations in EP300 or CREBBP, that we found in ~1.2% of MDS/MPN/AML in our (n=690) and other series. Consistent with pan-lineage-maturation needs for H3K27ac, the mutations were found in all lineage sub-types. H3K27me3 ‘off’ mark was mostly erased from myeloid programs in HSPC, but was greater at MK vs erythroid, and also at mono vs granulocyte genes (Fig.1B). This implied more need for H327me3 demethylase (KDM6A/UTX) for HSPC commitment into MK vs erythroid, or mono vs granulocyte, lineages. Accordingly, KDM6A was most upregulated in MK and mono-lineage cells (Fig.1C), and myeloid-conditional Kdm6a knockout decreased platelets and increased red cells in the spleen - reported by others: https://doi.org/10.1182/blood.V128.22.1467.1467. RUNX1-ETO has been shown to specifically impede granulocytic but not mono differentiation - https://www.nature.com/articles/2403396 and KDM6A inactivating mutations were significantly more likely to occur secondary to RUNX1-ETO (4-9% BEAT and AMLSG case series) vs other cytogenetics (0.005%). Selection for KDM6A secondary mutations could thus be to channel myeloid precursors toward lineages most efficiently impeded by primary mutations. Although H3K27me3 was substantially erased at all myeloid-commitment and terminal programs (except MK) in HSPC vs ESC, subsequent lineage-commitment and maturation entailed rewriting H3K27me3 at preceding HSPC and alternate lineage-fate programs, including at MTF genes for alternate fates (Fig.1B, D). Primary MDS/MPN/AML and AML cell lines with inactivating mutations/deletions in H3K27 methyltransferase EZH2 thus displayed aberrant co-expression of lineage MTFs and gene expression programs of normally mutually exclusive lineages (Fig.1E-G).Conclusion. Epigenetic remodeling requirements vary by myeloid lineage and maturation stage. Thus, epigenetic enzyme mutations are selected by, and cause, lineage-context of transformation. This knowledge can guide choice of specific epigenetic enzyme inhibitors to remedy the lineage-maturation defects that drive and define myeloid malignancies. [Display omitted] DisclosuresHaferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Maciejewski: Novartis: Consultancy; Bristol Myers Squibb/Celgene: Consultancy; Regeneron: Consultancy; Alexion: Consultancy. Saunthararajah: EpiDestiny: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties.

Deficiency of RPS14 Beyond the Haploinsufficient Loss in Del(5q)

The prevailing theory in del(5q) is that haploinsuffciency (HI) stemming from deletion and not simply LOH (loss of heterozygosity) is the culprit in clonal evolution. To date no haploinsufficient gene has been found to be the leukemogenic factor conveying growth advantage, but various other genes have been found to be important for phenotypic features or for propensity to acquire subsequent specific lesions. RPS14 is an example of such a gene, particularly in patients (pts) with isolated del(5q), responsible for macrocytic anemia and erythroid dysplasia and a propensity for acquisition of TP53 mutations. We hypothesized that RPS14 downmodulation and its consequences may be more common than del(5q) and it is frequent pathophysiologic feature in MDS.We first analyzed the genomic and expression profile of 170 pts with del(5q) and 825 diploid for 5q. We developed a new analytic pipeline to identify the most HI genes present in a large number of del(5q) pts. Genes within CDR (common deleted region) were classified as HI from the linear model fit if (i) clonality vs. gene expression slope from the isolated del(5q) was negative and FDR<.05; and (ii) effect of del(5q) at 50% clonality vs. other cases was negative and FDR<.05. A total of 62 genes met these criteria for linear-model based genes HI status, with a further 5 genes dropping due to low expression. Gene expression for these 57 HI genes among del(5q) samples was adjusted to 50%-clonality using the slopes from the estimated linear model to remove clonal heterogeneity.After applying model-based sparse clustering approach on all cohort, we obtained 7 clusters (Figure 1). As expected, del(5q) cases clustered together and showed consistent HI of 5q marker gene expression. Cluster-1 (n=146) included almost all del(5q) cases, except for 8 “mis-categorized” patients. It was characterized by low risk MDS (LR-MDS), presence of anemia/neutropenia and low mutational burden, with TP53 being the most commonly mutated gene and the only cluster with CSNK1A1 mutations. The remaining non-del(5q) patients were grouped in 6 clusters. Diploid cluster-2 (n=133) featured a normal karyotype, frequent ASXL1 and TET2 mutations, and profound down-modulation of RPS14 in all the patients included in the cluster (vs. other diploid pts). While the median RPS14 expression in cluster-1 (del(5q) cluster, with 50% adjusted clonality) was 7.29 (range 4.68-8.82 Log 2CPM), cluster-2 exhibited a median RPS14 expression of 6.12 Log 2CPM (range: 4.91-7.31 Log 2CPM). Clusters-3, -4, -5 (n=138, 90, 94, respectively) included most of the high risk MDS (HR-MDS). Cluster-3 was enriched for thrombocytopenia and SRSF2 mutations; cluster-4 for anemia, thrombocytopenia and ASXL1 and SRSF2 mutations. Cluster-5 was characterized by pancytopenia and frequent ASXL1 mutations and CK (complex karyotype). Cluster-6 (n=66) and -7 (n=233) contained the majority of non-del(5q) LR-MDS. When we analyzed the RPS14 expression in these clusters based on the RPS14 expression in cluster 2 we found 13% (n=18), 21% (n=19), 9% (n=8), 14% (n=9), 7% (n=16) of low RPS14 expressors in cluster-3, -4, -5, -6, -7, respectively.Cluster-2 showed a similar percentage of patients with anemia, and thrombocytopenia vs. Cluster-1 (69 vs. 50%, 23 vs. 30%; respectively). The mutational profile included a higher frequency of mutations for SRSF2 (29 vs. 0%), NRAS/KRAS (22% vs. 4%), ASXL1 (40 vs. 15%), TET2 (35 vs. 15%), and JAK2 (17 vs. 6%). These results indicate a more proliferative molecular spectrum of RPS14 downregulated cluster-2 than del(5q)-cluster-1, but RPS14 downmodulation did not lead to acquisition of TP53 mutations (4% vs. 76%). Considering all non-del(5q) RPS14 low expressors (n=186), only 3% of the cases had TP53 mutations. Since TP53 and CSNK1A1 mutations were characteristic of cluster-1 we studied interactions with HI RPS14. HI RPS14 in del(5q) and diploid low expressors showed a decreased expression of CDKN1A (P<.001) in comparison to the non-HI or low RPS14. We also found that CSNK1A1 mutations were not found outside of del(5q) pts, CSNK1A1 low expressors coincided with RPS14 low expressors.In conclusion, RPS14 expression defect is more widespread than del(5q) in MDS. However, only del(5q) RPS14 HI pts are prone to harbor TP53 and CSNK1A1 mutations; a group of diploid pts with low RPS14 and CSNK1A1 expressions might mimic some del5q features and could potentially respond to similar treatments. [Display omitted] DisclosuresDiez-Campelo: Takeda Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Carraway: AbbVie: Other: Independent review committee; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Other: Independent review committee; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Agios: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astex: Other: Independent review committee; Stemline: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene, a Bristol Myers Squibb company: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Maciejewski: Bristol Myers Squibb/Celgene: Consultancy; Regeneron: Consultancy; Novartis: Consultancy; Alexion: Consultancy.

TERT Rare Variants in Myeloid Neoplasia: Lack of Clinical Impact or Role as Risk Alleles

▪Dyskeratosis congenita is a prototypic inherited telomeropathy. Telomere length (TL) shortening has also been described in aplastic anemia (AA) and attributed in occasional patients to the presence of mostly heterozygous germline alterations in telomerase machinery genes (most commonly in the reverse transcriptase gene, TERT). Among various heterozygous variants described, certain TERT variants associated with shortened TL (e.g. H412Y and A202T) showed later a higher prevalence in general population questioning their pathogenicity and role as risk alleles. Nevertheless, shortened TL has been shown to correlate with poor outcomes in AA and myelodysplastic syndromes (MDS) along with a higher progression rate to acute myeloid leukemia, and increased non-relapse mortality after HSCT. A high prevalence (41/1514; 2.7%) of non-recurrent germline TERT rare variants, has been found in patients with MDS undergoing HSCT (Reilly et al Blood 2021) without any clinically apparent diagnosis of a telomere biology disorder. Despite their classification as VUS, these alterations were shown to result in impairment of telomere elongation in vitro and reduced TL in vivo. Clinically, VUS carriers were also characterized by younger age at MDS presentation (median 52 years) and shorter survival due to a higher incidence of non-relapse mortality.We hypothesized that should such rare variants (traditionally thought as VUS) represent founder lesions and constitute risk alleles, one would expect to find some recurrence of these lesions in other cohorts of patients with myeloid neoplasia (MN). To test this hypothesis we screened a large meta-analytic cohort of MN patients (n=2560) including our patients (The Cleveland Clinic Foundation and MLL-Munich Leukemia laboratory) and a public series (The BEAT AML Master Trial and TCGA among others) for the presence of TERT coding variants. When whole genome sequencing was available (n=1432) TelomereHunter tool was used to estimate TL in carriers of TERT rare variants and wild-type (WT) cohorts.Overall, we identified 73 TERT coding variants. Based on maximum gnomAD population allele frequency (AF) <0.001 and ACMG and pathogenicity scores according to the classifiers of VarSome (https://varsome.com), we found 37/2560 (1.4%) variants which were categorized as rare and VUS. Of note, only 6 patients harbored established TERT pathogenic variants while the rest were common and VUS. Alterations occurred in the RTD (52%), TRBD-Linker region (29%) and CTE domain (19%). When compared to the aforementioned HSCT cohort described by Reilly et al, TERT rare VUS had a lower frequency and did not overlap with any of the VUS reported in this study, except for 2 patients harboring one variant each in amino acid locations 741 and 1041. Assessment of TL, possible in 12 patients carrying TERT rare variants, did not reveal differences compared to TERT WT cases. Interestingly, none of these rare variants were also observed in a control cohort of patients with AA (n=268).In terms of clinical features, MN patients with TERT rare variants harbored more somatic hits in DNMT3A and SF3B1 (26% each), and FLT3 (15%) with a diverse spectrum of additional molecular lesions, including germline VUS of other genes (4 patients harbored concomitant VUS in BRCA1, FANCG, and SAMD9, with another patient carrying a likely benign SAMD9L variant) (Fig1). When compared to WT counterparts, patients harboring TERT rare variants had a similar median age at MN diagnosis (70 vs. 66, P= 0.38) and similar survival outcomes (P= 0.96). Importantly, HSCT outcomes were ascertained only in 7 cases because of the real-life nature of our cohort which compiles MN patients of any subtype and age, thereby not biased by transplant referral like in the aforementioned study focusing only on MDS patients undergone HSCT. Finally, principal causes of death were primary disease (63%), presence of another malignancy (19%) and infections (13%) and, of note, no patient died of non-infectious pulmonary causes.In sum, our study, while confirming a relatively high (albeit not significant vs. controls) combined TERT VUS population burden in MN, also strongly indicates that rare TERT VUS are non-recurrent. Therefore, they cannot be considered risk alleles individually nor can their biological (combined or individual) impact in terms of non-transplant outcomes, irrespective of their in vitro functional effects. [Display omitted] DisclosuresCarraway: Celgene, a Bristol Myers Squibb company: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Stemline: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Agios: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Other: Independent review committee; Takeda: Other: Independent review committee; Astex: Other: Independent review committee; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Maciejewski: Bristol Myers Squibb/Celgene: Consultancy; Alexion: Consultancy; Novartis: Consultancy; Regeneron: Consultancy.

Impact of Gender on Molecular AML Subclasses - a Harmony Alliance Study

The large acute myeloid leukemia (AML) patient-derived data sets collected within the European HARMONY alliance allows to study the molecular heterogeneity underlying AML in detail. Especially, how cytogenetic and molecular genetic aberrations differentially affect patients. Here, we report first results on the differences in mutational patterns in males and females. We studied a cohort of AML patients characterized by a panel of 70 molecular abnormalities comprising both cytogenetic and genetic observations. We quantified the differences of molecular patterns between sexes in two ways: 1) by comparing the number of gene-gene mutation co-occurrence and mutual mutation exclusivity with a χ 2 test, 2) exploiting the Hierarchical Dirichlet process (HDP) for molecular components discovery. In particular, we added sex as a further layer of the hierarchy allowing the same molecular components to be differently re-weighted based on gender. The HARMONY AML cohort comprised 2796 patients with detailed molecular information from targeted sequencing of 41 genes and detailed cytogenetic information condensed into 29 cytogenetic properties, known, a priori, to be relevant for the disease. Male to female ratio was 52% vs. 48% and median age was 52.0 (18.2 - 91.4) years. The entry data of the analysis were in the form of a binary matrix reporting the presence/absence of a given alteration in a patient. The χ 2 test based on the relative co-occurrence of mutation pairs suggested a significant difference between men and women solely for RUNX1 and NPM1. The number of co-occurrences was higher in male than in females. No significant mutual exclusive mutations were found between the populations. By using a two hierarchic levels HDP clustering we identified 11 overall molecular components shared by all AML patients. Six of these components are characterized by one or more genetic drivers, namely: NPM1, RUNX1, Complex-Karyotype-TP53, FLT3-IDH2, IDH2, CEPBA-biallelic, while the others were driven by cytogenetic abnormalities: t(6;9), t(8;21), inv(3), rearrangement of 11q23, inv(16). These results were in agreement with the current WHO AML classification and with other recent studies, which have attempted to improve stratification/classification of patients based on their molecular aberration patterns. While the molecular components were the same for all patients, major differences were observed in the contribution of NPM1 and RUNX1 components to males' and females' genotypes. On one side, NPM1 component has a double weight in females with respect to males. On the other hand, RUNX1 impacts males much more than females. The other aberrations were equally represented in both sexes. To test the robustness of the differences found between sexes, we compared these results with random splits of the datasets finding no differences in component weights, thereby validating our observations.Big data collections such as the HARMONY Alliance data base ensure data comparability via OMOP common data model harmonization approaches thereby offering the possibility to study large cohort that allow meaningful subgroup analyses such as the one focusing on gender imbalances. Proving the concept of the HARMONY Alliance data hub, our study confirms a female preponderance for NPM1 mutations and an association of RUNX1 mutations with male gender. Impact on patient outcome is currently evaluated and will be presented at the annual meeting. [Display omitted] DisclosuresHeckman: Celgene/BMS: Research Funding; Orion Pharma: Research Funding; Novartis: Research Funding; Oncopeptides: Consultancy, Research Funding; Kronos Bio, Inc.: Research Funding. Dombret: Abbvie: Honoraria; Amgen: Honoraria, Research Funding; Incyte: Honoraria, Research Funding; Jazz Pharmaceuticals: Honoraria, Research Funding; NOVARTIS: Research Funding; pfizer: Honoraria, Research Funding; servier: Research Funding; BMS-Celgene: Honoraria; Daiichi Sankyo: Honoraria. Montesinos: Tolero Pharmaceutical: Consultancy; Agios: Consultancy; Stemline/Menarini: Consultancy; Teva: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Forma Therapeutics: Consultancy; Glycomimetics: Consultancy; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astellas Pharma, Inc.: Consultancy, Honoraria, Other: Advisory board, Research Funding, Speakers Bureau. Sierra: Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Honoraria, Research Funding, Speakers Bureau; Jazz Pharmaceuticals: Research Funding; Janssen: Other: Educational grant; Roche: Other: Educational grant; Pfizer: Honoraria; BMS Celgene: Honoraria, Research Funding; Alexion: Other: Educational grant; Amgen: Other: Educational grant; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Mayer: Principia: Research Funding. Voso: Novartis: Speakers Bureau; Celgene: Consultancy, Research Funding, Speakers Bureau. Sanz: Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses, Speakers Bureau; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Boehringer Ingelheim: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead Sciences: Other: Travel, accommodations, and expenses; Helsinn Healthcare: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses, Research Funding. Calado: Novartis: Current Employment. Döhner: Agios and Astex: Research Funding; Daiichi Sankyo: Honoraria, Other: Advisory Board; Jazz Roche: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Janssen: Honoraria, Other: Advisory Board; Astellas: Research Funding; Celgene/BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Gaidzik: Pfizer: Speakers Bureau; Janssen: Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Heuser: BergenBio: Research Funding; Jazz Pharmaceuticals: Honoraria, Research Funding; BMS/Celgene: Research Funding; Bayer AG: Honoraria, Research Funding; Astellas: Research Funding; Daichi Sankyo: Honoraria, Research Funding; Karyopharm: Research Funding; Novartis: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Roche: Research Funding; Tolremo: Honoraria; AbbVie: Honoraria; Janssen: Honoraria. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Sobas: Celgene: Consultancy, Honoraria; Novartis: Consultancy, Honoraria. Turki: CSL Behring: Consultancy; Jazz Pharma: Consultancy, Speakers Bureau; MSD: Consultancy, Speakers Bureau. Schulze-Rath: Bayer: Current Employment. Hernández Rivas: Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene/BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees. Döhner: Abbvie: Honoraria, Research Funding; Agios: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Astellas: Honoraria, Research Funding; Astex Pharmaceuticals: Honoraria; AstraZeneca: Honoraria; Berlin-Chemie: Honoraria; Bristol Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; GEMoaB: Honoraria; Gilead: Honoraria; Helsinn: Honoraria; Janssen: Honoraria; Jazz Pharmaceuticals: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Oxford Biomedica: Honoraria; Pfizer: Research Funding; Roche: Honoraria. Ossenkoppele: Abbvie, AGIOS, BMS/Celgene Astellas,AMGEN, Gilead,Servier,JAZZ,Servier Novartis: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; BMS/Celgene: Consultancy, Honoraria; Jazz: Consultancy, Honoraria; Gilead: Consultancy, Honoraria. Bullinger: Menarini: Consultancy; Amgen: Honoraria; Hexal: Consultancy; Novartis: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Daiichi Sankyo: Consultancy, Honoraria; Astellas: Honoraria; Abbvie: Consultancy, Honoraria; Gilead: Consultancy; Sanofi: Honoraria; Seattle Genetics: Honoraria; Bayer: Research Funding.

Multicenter Prospective Evaluation of Diagnostic Potential of Flow Cytometric Aberrancies in Myelodysplastic Syndromes

Background: Myelodysplastic syndromes (MDS) are considered clonal diseases and are diagnosed according to WHO by cytomorphology and cytogenetics. The diagnostic potential of flow cytometric aberrancies has not yet been comprehensively evaluated.Aim: Multicenter prospective evaluation of diagnostic potential of flow cytometric aberrancies predefined according to European LeukemiaNet (ELN).Methods: 1682 patients undergoing diagnostics for suspected MDS according to WHO 2016 criteria were analyzed in parallel by flow cytometry according to ELN recommendations.Results: Median age was 72 years (18-97). MDS, MPN-RS-T or CMML were confirmed by cytomophology in 1029 (61%) cases, 653 (39%) were non-MDS. IPSS-R data was available in 857 (51%). An overall flow cytometric readout was available in 1679 (99.8%). 1001 (60%) were in agreement with MDS while 678 (40%) were not. Flow cytometric readout significantly correlated with cytomorphologic diagnosis (p<0.001): 850 (51%) were positive by both methods (flow+/cyto+), 502 (30%) were flow-/cyto-, 176 (10%) were flow-/cyto+ and 151 (9%) flow+/cyto-. The rate of flow+ was higher in high-risk MDS (MDS-EB1/2, 92%) and CMML (89%) compared to low-risk MDS (76%). Accordingly, regarding IPSS-R highest frequency of flow+ was found in very high risk (96%) and lowest one in very low risk group (64%).Non-MDS cases had a fewer myeloid progenitor cells (MPC) (mean±SD, 0.8±0.9%) compared to low-risk MDS (1.7±2.3%, p<0.001), high-risk MDS (6.3±5.0%, p<0.001) and CMML (1.9±2.6%, p<0.001). In particular, MPC >3% was strongly associated with MDS/CMML (286/293, 98%, p<0.001). Antigen expression aberrancies in MPC were more frequent in cases with MDS or CMML than in non-MDS cases but differed between entities with lower frequencies in low-risk MDS cases (table 1).Neutrophil aberrancies were found more frequently in neoplastic cases than in non-MDS cases (table 1). Again, frequencies of aberrations were higher for high-risk MDS as compared to low-risk MDS while this was not the case for CMML showing frequencies rather similar to low-risk MDS.Frequencies of aberrancies in monocytes revealed a similar figure as in neutrophils with higher rates in neoplastic cases but clearly significant numbers positive in non-MDS cases. Interestingly, frequencies were not higher in high-risk MDS as compared to low-risk MDS. As anticipated, frequencies were highest in CMML (table 1).Regarding erythroid cells only an aberrant percentage of them and aberrant CD71 expression were found in a reasonable number of cases. Importantly, rates of positivity were rather high in non-MDS cases which did not differ from CMML cases (table 1).In order to identify the diagnostic value of each individual aberrancy multivariate analyses were performed in the three subgroups, low-risk MDS, high-risk MDS and CMML, as well as in the total cohort. In low-risk MDS ten aberrancies were independently related to MDS (table 2). Five of these aberrancies were found in MPCs, two each in neutrophils and monocytes and one in erythroid cells. In high-risk MDS 11 aberrancies were independently related to MDS (table 2). Eight were found in MPCs, two in neutrophils, none in monocytes and one in erythroid cells. In CMML 12 aberrancies were independently related to CMML (table 2). Four were found in MPCs, neutrophils and monocytes, respectively, and none in erythroid cells. Considering all these three groups together and all aberrancies identified significantly related to MDS/CMML in at least one group in univariate analysis, multivariate analysis identified 12 aberrancies independently related to MDS/CMML (table 2). Six were found in MPCs, two in neutrophils, three in monocytes and one in erythroid cells.Taking into consideration only aberrancies independently associated with MDS/CMML, three such aberrancies resulted in an 80% agreement with the cytomorphologic diagnosis of MDS/CMML, i.e. 20% concordantly negative and 60% concordantly positive. Importantly, this applies without need of at least two cell compartments being affected as specified in the ELN recommendations.Conclusions: This multicenter prospective evaluation confirms the diagnostic potential of flow cytometric aberrancies. A core set of 17 markers identified as independently related to a diagnosis of MDS/CMML is suggested mandatory for flow cytometric evaluation of suspected MDS. An MPC count >3% should be considered indicative of MDS/CMML. [Display omitted] DisclosuresKern: MLL Munich Leukemia Laboratory: Other: Part ownership. Eidenschink Brodersen: Hematologics, Inc.: Current Employment, Other: Equity Ownership. Van de Loosdrecht: Celgene: Consultancy, Research Funding; Amgen: Consultancy; Roche: Consultancy; Novartis: Consultancy; Alexion: Consultancy.

Novel Synthetic Lethal Targets for Myeloid Neoplasms with Loss of Chromosome 7

Loss of chr7 (-7) and partial deletions of its long arm (del7q) are observed in 10% of de novo myeloid neoplasms (MNs), 50% of therapy related MDS, up to 60% of post aplastic anemia MNs and occur frequently as evolution of congenital bone marrow failure syndromes (e.g., GATA2 and SAMD9L deficiency, FA). LOH of one or more chr7 genes has been considered the culprit in the pathogenesis of -7/del7q MNs. In addition to the loss of protective alleles, deletion resulting in haploinsufficiency (HI) of tumor suppressor genes (TSGs) in CDRs might be also a cause of leukemogenic drive behind -7/del7q. To date, albeit many candidate genes have been associated with -7/del7q, the search for genes responsible for clinical phenotype has failed to identify causative -7/del7q TSGs and their putative loss would be difficult to target. Irrespective of the important goal of clarification of leukemogenic effects of -7/del7q, loss of genes in CDRs may create a vulnerability phenotype, which could be exploited with synthetic lethal approaches. Such strategies would rely on the higher resistance of diploid vs -7/del7q cells, thus allowing for a therapeutic window.Here, we studied the molecular profile of 8160 MN patients (del7q: 1.7%; -7: 6%). EZH2 mutations were enriched in -7/del7q compared to chr7 diploid cases (3.8 vs 1.2%, P<.0001) also by absolute numbers heterozygous mutations were more numerous. We also detected somatic CUX1 mutations (1.7 vs 0.9%), SAMD9/SAMD9L (0.3 vs 0.1%), and LUC7L2 (0.3 vs 0.1%) in -7/del7q vs diploid. In -7/del7 cases somatic alterations were detected in BRAF (n=7), POT1 (n=3), PCLO (n=5) and PSMC2 (n=1) while no mutations in CUL1 and KMT2C were found. We then investigated the presence of driver mutations located on other chromosomes in -7/del7q. Del7q/-7 cases showed a lower frequency of TET2 and SF3B1 mutations vs diploid cases. In isolated/+1 del7q/-7 cases, TP53 mutations were significantly less frequent, but were increased in -7/del7q with complex karyotype (P<.0001). Higher frequencies of RAS genes, RUNX1 and ETV6 hits were also found. Del7q and TP53 mutations were founder lesions (dominant) in 38% and 54% of -7/del7q, while -7 was dominant in 63% of -7 cases. TP53 was the only mutation significantly associated with further worsening the already poor prognosis of -7/del7q cases (HR=1.629 P< .01). Germline alterations were more common in -7/del7q as compared to diploid cases (13 vs 5% P< .0001) of which most were FA or DNA repair gene variants also in other genes including (e.g., SAMD9L, 7%, DDX41, 3.7%).Having defined the genotype of -7/del7q, we set to identify genes which could be possible targets for the therapy chiefly synthetic lethality. Criteria for selection included: consistent HI in most of the patients, genes not affected by hemizygous LOF mutations and embryonic lethality in knockout (KO) configuration. Expression data of -7/del7q (n=86), diploid cases (n=1066) and healthy controls (n=84; MLL and BEAT AML to increase precision) were analyzed. Our algorithm included selection of genes with mRNA expression inversely correlating with copy number (deletion copy number). Out of 694 genes on chr7, 147 genes were deleted in all patients and 101 genes had more inconsistent HI levels. In total 35 genes showed significant negative correlation with -7/del7q ploidyincluding ACTR3B,AGK,ATP06V0E2,CUL1,FASTK,GALNT11,GSTK1, IMPDH1, PLXNA4, SLC37A3, ZNF277, KMT2C, NUP205, TMEM209, ZC3HC1 and GIMAP1/2/4/6, a cluster ofnucleotide binding proteins. Following adjustment to ploidy, HI was found for EZH2 (76% cases), CUX1 (76%), KMT2C (70%), LUC7L2 (60%), and SAMD9/9L (32%/50%) but also even more consistently in SSBP1 (88%), PSMC2 (86%), CUL1, ZNF398, and RHEB (all 84%) and TNPO3 (82%). Among those genes homozygous KO of Ezh2 and Cul1 lead to embryonic lethality, Gimap family deletion reduces normal hematopoiesis, Samd9l +/-and Samd9l-/- mice develop MDS and die after 1.5yrs and Cux1 knockdown causes an MDS like phenotype. Existing inhibitors are available for CUL1 (MLN4924), CUX1 (BER modulating agents) and EZH2 (EPZ6438, GSK343), but the presence of homozygous mutations (UPD7q) argues that EZH2 inhibition is unlikely to be successful.In conclusion, we showed a comprehensive molecular topography of -7/del7q and identified novel HI genes which could be targeted by novel or repurposed drugs. Ongoing drug screens for identified targets performed in cells with -7/del7q will be presented at the meeting. DisclosuresHaferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Maciejewski: Bristol Myers Squibb/Celgene: Consultancy; Novartis: Consultancy; Regeneron: Consultancy; Alexion: Consultancy.

A Novel Machine Learning-Derived Molecular Classification Scheme with Prognostic Significance

The gold standard for the diagnosis of MDS relies on morphologic alterations hampered by a great deal of subjectivity. Cytogenetic and clinical features allow for clinical classifications predictive of survival. However, with a few exceptions (SF3B1MT, del5q, and certain balanced translocations), neither classic histo-morphology nor prognostic scoring systems (e.g., IPSS-R) are reflective of pathogenic underpinnings. To date supervised analyses of mutational data did not succeed to produce profiles specific or predictive of traditional disease sub-entities. Large cohorts with clinical annotation and a sufficient follow-up allow for innovative biostatistical approaches to subgroup patients according to molecular profiles. Objective operator-independent subcategorization may be congruent with common pathogenic links, rational applications of targeted therapeutics and better prognostications.We hypothesized that machine-learning (ML) strategies used for analysis of mutational/cytogenetic profiles will enable recognition of invariant disease subcategories according to their molecular configurations. Herein, we compiled a meta-analytic database (our cohorts and publicly available sources) of 3,011 MDS (median age 71yrs) and 6,788 pAML/sAML. Results of deep targeted sequencing of a panel of 55 myeloid mutations were collected together with cytogenetics. We then performed unsupervised analysis of MDS and AML patients using Bayesian Latent Class Analysis (BLCA). A consensus matrix was then clustered using Ward's criteria to generate final cluster assignment based on the highest silhouette value. To identify genomic signatures, we used Random Forest classification and extracted mutations with highest global importance indicated by mean decrease in accuracy.Using BLCA we identified 5 unique genomic clusters (GCs) with 3 distinct prognostic outcomes [low risk (LR), intermediate risk (Int), and high risk (HR)] that were validated by survival analysis (Fig.1A,B). The LR group included GC-1 and was characterized by the highest prevalence of normal cytogenetics (100%) and SF3B1 MT (25%) with co-occurring DNMT3A MT (14%), and absence of ASXL1 MT, ETV6 MT, STAG2 MT, TP53 MT, and complex/abnormal cytogenetics. Int group included GC-2 and GC-4. GC-2 was characterized by a higher percentage of abnormal cytogenetics cases than LR group and absence of STAG2 MT, SRSF2 MT, ASXL1 MT, TP53 MT, and normal/complex cytogenetics. GC-4 had the highest frequency of SRSF2 MT (52%) with co-occurring ASXL1 MT (59%), TET2 MT (40%), normal karyotype, and absence of complex/abnormal cytogenetics. Finally, HR included GC-3 and GC-5. GC-3 included ASXL1 MT (67%) with co-occurring SRSF2 MT (47%), TET2 MT (37%), STAG2 MT (22%), and absence of normal cytogenetics. GC- 5 had the highest frequency of -5/del(5q) (50%), -7/del(7q) (43%), -17/del(17p) (16%) and the highest odds of complex karyotype (92%) as well as TP53 MT (48%). Paralleling the genomic ML-based clustering, the clinical relevance of these subgroups was reflected in significantly different survivals [median (95% CI)]: i) GC-1 [69 (59-80)], ii) GC-2 [35 (29-41)], iii) GC-3 [12 (10-16)], GC-4 [27 (22-34)], and GC-5 [9 (7-11)] months (Fig.1C). We then classified the MDS cohort according to the recently published and validated AML GCs (Awada et al Blood 2021) to investigate overlapping genomic features. Overall, 90% of MDS GC-1 and 67% of MDS GC-2 had the same molecular architecture of AML GC-2 and 70% of MDS GC-5 had the same molecular features of AML GC-4. In addition, 98% of MDS GC-3 and 92% of MDS GC-4 had the same features of AML GC-3 (Fig.1D).In sum, we propose a novel objective molecular classification of MDS and related diseases that allows subgrouping of patients according to shared pathogenesis for a better prognostic resolution without errors derived from subjectivity. The model was then internally and externally validated using a cohort of 200 cases. Results of a validation cohort and online URL site of molecular clustering will be presented at the meeting. [Display omitted] DisclosuresBalasubramanian: Servier Pharmaceuticals: Research Funding. Patel: Alexion: Consultancy, Other: educational talks, Speakers Bureau; Apellis: Consultancy, Other: educational talks, Speakers Bureau. Carraway: Celgene, a Bristol Myers Squibb company: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astex: Other: Independent review committee; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Agios: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Other: Independent review committee; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Other: Independent review committee; Stemline: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Maciejewski: Regeneron: Consultancy; Novartis: Consultancy; Alexion: Consultancy; Bristol Myers Squibb/Celgene: Consultancy.