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Acute myeloid leukemia (AML) is a hematological malignancy with an undefined heritable risk. Here we perform a meta-analysis of three genome-wide association studies, with replication in a fourth study, incorporating a total of 4018 AML cases and 10488 controls. We identify a genome-wide significant risk locus for AML at 11q13.2 (rs4930561; P = 2.15 × 10−8; KMT5B). We also identify a genome-wide significant risk locus for the cytogenetically normal AML sub-group (N = 1287) at 6p21.32 (rs3916765; P = 1.51 × 10−10; HLA). Our results inform on AML etiology and identify putative functional genes operating in histone methylation (KMT5B) and immune function (HLA)., Genome wide association studies in cancer are used to understand the heritable genetic contribution to disease risk. Here, the authors perform a genome wide association study in European patients with acute myeloid leukemia and identify loci associated with risk of developing the disease.

PURPOSE Patients with myelodysplastic syndromes (MDS) have a survival that can range from months to decades. Prognostic systems that incorporate advanced analytics of clinical, pathologic, and molecular data have the potential to more accurately and dynamically predict survival in patients receiving various therapies. METHODS A total of 1,471 MDS patients with comprehensively annotated clinical and molecular data were included in a training cohort and analyzed using machine learning techniques. A random survival algorithm was used to build a prognostic model, which was then validated in external cohorts. The accuracy of the proposed model, compared with other established models, was assessed using a concordance (c)index. RESULTS The median age for the training cohort was 71 years. Commonly mutated genes included SF3B1, TET2, and ASXL1. The algorithm identified chromosomal karyotype, platelet, hemoglobin levels, bone marrow blast percentage, age, other clinical variables, seven discrete gene mutations, and mutation number as having prognostic impact on overall and leukemia-free survivals. The model was validated in an independent external cohort of 465 patients, a cohort of patients with MDS treated in a prospective clinical trial, a cohort of patients with paired samples at different time points during the disease course, and a cohort of patients who underwent hematopoietic stem-cell transplantation. CONCLUSION A personalized prediction model on the basis of clinical and genomic data outperformed established prognostic models in MDS. The new model was dynamic, predicting survival and leukemia transformation probabilities at different time points that are unique for a given patient, and can upstage and downstage patients into more appropriate risk categories.

Background Considering the clinical and genetic characteristics, acute lymphoblastic leukemia (ALL) is a rather heterogeneous hematological neoplasm for which current standard diagnostics require various analyses encompassing morphology, immunophenotyping, cytogenetics, and molecular analysis of gene fusions and mutations. Hence, it would be desirable to rely on a technique and an analytical workflow that allows the simultaneous analysis and identification of all the genetic alterations in a single approach. Moreover, based on the results with standard methods, a significant amount of patients have no established abnormalities and hence, cannot further be stratified. Methods We performed WTS and WGS in 279 acute lymphoblastic leukemia (ALL) patients (B-cell: n = 211; T-cell: n = 68) to assess the accuracy of WTS, to detect relevant genetic markers, and to classify ALL patients. Results DNA and RNA-based genotyping was used to ensure correct WTS-WGS pairing. Gene expression analysis reliably assigned samples to the B Cell Precursor (BCP)-ALL or the T-ALL group. Subclassification of BCP-ALL samples was done progressively, assessing first the presence of chromosomal rearrangements by the means of fusion detection. Compared to the standard methods, 97% of the recurrent risk-stratifying fusions could be identified by WTS, assigning 76 samples to their respective entities. Additionally, read-through fusions (indicative of CDKN2A and RB1 gene deletions) were recurrently detected in the cohort along with 57 putative novel fusions, with yet untouched diagnostic potentials. Next, copy number variations were inferred from WTS data to identify relevant ploidy groups, classifying an additional of 31 samples. Lastly, gene expression profiling detected a BCR-ABL1-like signature in 27% of the remaining samples. Conclusion As a single assay, WTS allowed a precise genetic classification for the majority of BCP-ALL patients, and is superior to conventional methods in the cases which lack entity defining genetic abnormalities.

Acute myeloid leukemia (AML) is a hematological malignancy with an undefined heritable risk. Here we performed a meta-analysis of three genome-wide association studies, with replication in a fourth study, incorporating a total of 4018 AML cases and 10488 controls. We identified a genome-wide significant risk locus for AML at 11q13.2 (rs4930561; P = 2.15 × 10-8; KMT5B). We also identified a genome-wide significant risk locus for the cytogenetically normal AML sub-group (N=1287) at 6p21.32 (rs3916765; P = 1.51 × 10-10; HLA). Our results inform on AML etiology by identifying putative functional genes operating in histone methylation (KMT5B) and immune function (HLA).

Genomic alterations are a hallmark of hematological neoplasms and comprise small nucleotide variants, copy number alterations, and structural variants (SV). The latter can arise by events within on...

Aberrant somatic hypermutation (aSHM) can target proto-oncogenes and drive oncogenesis. In mantle cell lymphoma (MCL), CCND1 is targeted by aSHM in the non-nodal subtype (nnMCL), giving rise to exon1 encoded mutant proteins like E36K, Y44D, and C47S that contribute to lymphomagenesis by virtue of their increased protein stability and nuclear localization. However, the vast majority of somatic variants generated by aSHM are found in the first intron of CCND1 but their significance for mantle cell lymphomagenesis is unknown. We performed whole-genome and whole-transcriptome sequencing in 84 MCL patients to explore the contribution of non-coding somatic variants created by aSHM to lymphomagenesis. We show that non-coding variants are enriched in a MCL specific manner in transcription factor-binding sites, that non-coding variants are associated with increased CCND1 mRNA expression, and that coding variants in the first exon of CCND1 are more often synonymous or cause benign amino acid changes than in other types of lymphomas carrying a t(11;14) translocation. Therefore, the increased frequency of somatic variants due to aSHM might be a consequence of selection pressure manifested at the transcriptional level rather than being a mere mechanistic consequence of misguided activation-induced cytidine deaminase (AID) activity.

Precision medicine can significantly improve outcomes for cancer patients, but implementation requires comprehensive characterization of tumor cells to identify therapeutically exploitable vulnerabilities. Here we describe somatic biallelic TET2 mutation (focal deletion and nonsense mutation) in an elderly patient with acute myeloid leukemia (AML) that was chemoresistant to anthracycline and cytarabine, but acutely sensitive to 5’-azacitidine (5’-Aza) hypomethylating monotherapy, resulting in long-term morphological remission (overall survival (OS) 850 days). Given the role of TET2 as a regulator of genomic methylation, we hypothesized that mutant TET2 allele dosage affects response to 5’-Aza. Using an isogenic cell model system and an orthotopic mouse xenograft, we demonstrate that biallelic TET2 mutations confer sensitivity to 5’-Aza compared to cells with monoallelic mutation. We subsequently identified 29 additional patients from the Study Alliance Leukemia biobank with chromosome 4 abnormalities and identified two further patients with complex biallelic TET2 mutations, including one with trisomy 4, homozygosity across the long arm and an inactivating point mutation. We also screened patients recruited to the PETHEMA FLUGAZA phase 3 clinical trial and identified three patients with biallelic TET2 mutations, two of whom had responded very well to single agent 5’-Aza (OS 767 and 579 days) despite having adverse risk AML and poor performance status. Our data argue in favor of using hypomethylating agents for chemoresistant disease or as first line therapy in patients with biallelic TET2-mutated AML and demonstrate the importance of considering mutant allele dosage in the implementation of precision medicine for cancer patients.Key PointsMutant TET2 allele dosage affects response to 5’-azacitidine in acute myeloid leukemia in vitro and in a xenograft model.Our data highlight the importance for screening of biallelic mutations to predict response to therapy in acute myeloid leukemia.

Background While histo- and cytomorphological examinations are central to the diagnosis of myelodysplastic syndromes (MDS), significant inter-observer variability exists. The diagnosis can be challenging in pancytopenic patients (pts) without evidence of dysplasia and is contingent on observer expertise. We developed and externally validated a geno-clinical model that uses mutational data and peripheral blood counts/clinical variables to distinguish MDS from other myeloid malignancies. Methods Clinical and genomic data, including commercially available next-generation sequencing panels, were obtained for patients (pts) treated at the Cleveland Clinic (CC; 652 pts), Munich Leukemia Laboratory (MLL; 1509 pts), and the University of Pavia in Italy (UP, 536 pts). All patients had carried a diagnosis of MDS, chronic myelomonocytic leukemia (CMML), MDS/myeloproliferative neoplasm overlap (MDS/MPN), myeloproliferative neoplasm (MPN; either polycythemia vera, essential thrombocythemia, or myelofibrosis), clonal cytopenia of undetermined significance (CCUS), or idiopathic cytopenia of undetermined significance (ICUS). All diagnoses were established with bone marrow aspiration and according to World Health Organization 2017 criteria. The training cohort included data from CC and UP and randomly divided into learner (80%) and test (20%) cohorts. The final model was independently validated in the MLL cohort. A machine learning algorithm was used to build the model; multiple extraction algorithms were used to extract genomic/clinical variables on both the cohort and individual levels. Performance was evaluated according to the area under the curve of the receiver operating characteristic (ROC-AUC) and accuracy matrices. Results Among the 2697 pts included from all sites, the median age was 70 years [36 - 86]. Median hemoglobin (Hb) was 10.4g/dl [6.9 - 15.7], median platelet count (PLT) was 132 k/dL [14 - 722], median WBC count was 5.3 k/dL [1.4 - 49.9], median ANC was 2.8 k/dL [0.3 - 27.7], median monocyte count was 0.3 k/dL [0 - 9.9], and median lymphocyte count (ALC) was 1.1 k/dL [0.1 - 5.4], and median peripheral blast percentage 0% [0 - 8]. The most commonly mutated genes in all patients were (list top 5 genes) and among pts with MDS were SF3B1 (27%), TET2 (25%), ASXL1 (19%), SRSF2 (16%), and DNMT3A (11%); among patients with MDS-MPN/CMML, the most commonly mutated genes were MDS-MPN/CMML (TET2 46%, ASXL1 34%, SRSF2 29%, RUNX1 13%, CBL 12%) ; among patients with MPNs, the most commonly mutated genes were (JAK2 64%, ASXL1 27%, TET2 14%, DNMT3A 8%, U2AF1 7%); among patients with CCUS the most commonly mutated genes were (TET2 41%, DNMT3A 27%, ASXL1 19%, SRSF2 17%, ZRSR2 10%). The most important features for model predictions (ranked from the most to the least important) included: number of mutations detected/sample, peripheral blast percentage, AMC, JAK2 status, Hb, basophil count, age, eosinophil count, ALC, WBC, EZH2 mutation status, ANC, mutation status of KRAS and SF3B1, platelets, and gender. The final model achieved an average AUROC of 0.95 (95% CI 0.93-0.96) when applied to the test cohort and 0.93 (95% CI 0.91 - 0.94) when it was applied to the MLL cohort. The model also provides individual-level explanations for predictions, providing top differential diagnoses and individual-level explanations of how features influence a putative diagnosis (Figure 1b). Conclusions We developed and externally validated a highly accurate and interpretable model that can distinguish MDS from other myeloid malignancies using clinical and mutational data from a large international cohort. The model can provide personalized interpretations of its outcome and can aid physicians and hematopathologists in recognizing MDS with high accuracy when encountering pts with pancytopenia and with a suspected diagnosis of MDS. Disclosures Sekeres: Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda/Millenium: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees. Mukherjee:Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Honoraria; Bristol Myers Squib: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Aplastic Anemia and MDS International Foundation: Honoraria; Celgene/Acceleron: Membership on an entity's Board of Directors or advisory committees; EUSA Pharma: Consultancy. Gerds:Sierra Oncology: Research Funding; Imago Biosciences: Research Funding; Apexx Oncology: Consultancy; Celgene: Consultancy, Research Funding; Incyte Corporation: Consultancy, Research Funding; Roche/Genentech: Research Funding; CTI Biopharma: Consultancy, Research Funding; AstraZeneca/MedImmune: Consultancy; Gilead Sciences: Research Funding; Pfizer: Research Funding. Maciejewski:Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria. Nazha:Jazz: Research Funding; Incyte: Speakers Bureau; Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee.

Therapy-related myeloid neoplasms (tMN) following successful treatment of acute myeloid leukemia (AML) are rare and poorly characterized. To evaluate the presence of a common ancestral clone, we performed whole-exome sequencing of 25 patients at AML diagnosis, tMN diagnosis (tMDS: 13; tAML: 12), and matched remission samples, identifying 607 mutations affecting 504 different genes (46 recurrently mutated). Number of mutations was higher in tAML vs. tMDS cases (median 19 vs 13 mutations, p = 0.05). Focusing on 24 genes commonly mutated in hematological malignancies, 19/25 (76%) patients were found to share mutations between AML and tMN, mostly affecting epigenetic modifiers (21/32; 66%), splicing factors (6/32; 19%), and chromatin modifiers (3/32; 9%). Analysis of remission samples identified 13 persisting mutations in 10/22 patients, affecting DNMT3A (n = 6), TET2 (n = 5), IDH1 and SRSF2 (n = 1, each). Comparison of cytogenetics revealed that 9/12 patients with a normal karyotype (NK) in AML harbored aberrations in tMN, four aberrant AML cases presented with NK in tMN, four other patients showed unrelated cytogenetic aberrations. Our study provides novel insights into the pathogenesis of tMN, hypothesizing the presence of a common ancestral clone in AML and tMN. Mutations mostly affected epigenetic modifiers, which have previously been linked to clonal hematopoiesis.

Deletions in the long arm of chromosome 7 (del(7q)) are recurrent cytogenetic aberrations in myeloid neoplasms. They occur either isolated or as part of a complex karyotype and are associated with unfavorable prognosis in certain disease entities. We performed detailed cytogenetic analysis, molecular analysis, and array comparative genomic hybridization in a cohort of 81 patients with a variety of myeloid malignancies and del(7q) as sole chromosomal alteration. In 70% (57/81) of patients, we identified a commonly deleted region (size: 18 Mb) involving the genomic region 101 912.442 (7q22.1)-119 608.824 (7q31.31). Furthermore, in 80 patients, we analyzed 17 genes commonly mutated in myeloid neoplasms and identified high mutation frequencies in ASXL1 34% (27/80), TET2 33% (26/80), RUNX1 25% (20/80), DNMT3A 25% (20/80), while TP53 was rarely affected (5%, 4/80). ASXL1 and TET2 showed similar mutation frequencies across all analyzed entities while RUNX1, CBL, and JAK2 were specifically mutated in patients with acute myeloid leukemia (AML), chronic myelomonocytic leukemia, and myeloproliferative neoplasms, respectively. We detected a significantly higher frequency of RUNX1 (42% vs 13%, P = .0001) and ASXL1 (32% vs 14%, P = .008) mutations in AML patients with del(7q) compared to other AML patients in the Medical Research Council unfavorable risk group (n = 464), indicating a cooperative leukemogenic potential. Our data provide further insight into the pathomechanism of this cytogenetic subgroup.

Myeloid neoplasms are characterized by frequent mutations in at least seven components of the spliceosome that have distinct roles in the process of pre-mRNA splicing. Hotspot mutations in SF3B1, SRSF2, U2AF1 and loss of function mutations in ZRSR2 have revealed widely different aberrant splicing signatures with little overlap. However, previous studies lacked the power necessary to identify commonly mis-spliced transcripts in heterogeneous patient cohorts. By performing RNA-Seq on bone marrow samples from 1258 myeloid neoplasm patients and 63 healthy bone marrow donors, we identified transcripts frequently mis-spliced by mutated splicing factors (SF), rare SF mutations with common alternative splicing (AS) signatures, and SF-dependent neojunctions. We characterized 17,300 dysregulated AS events using a pipeline designed to predict the impact of mis-splicing on protein function. Meta-splicing analysis revealed a pattern of reduced levels of retained introns among disease samples that was exacerbated in patients with splicing factor mutations. These introns share characteristics with "detained introns," a class of introns that have been shown to promote differentiation by detaining pro-proliferative transcripts in the nucleus. In this study, we have functionally characterized 17,300 targets of mis-splicing by the SF mutations, identifying a common pathway by which AS may promote maintenance of a proliferative state.

Chronic lymphoproliferative disorder of natural killer cells (CLPD-NK) is characterized by clonal expansion of natural killer (NK) cells where the underlying genetic mechanisms are incompletely understood. In the present study, we report somatic mutations in the chemokine gene CCL22 as the hallmark of a distinct subset of CLPD-NK. CCL22 mutations were enriched at highly conserved residues, mutually exclusive of STAT3 mutations and associated with gene expression programs that resembled normal CD16

Context: Chronic lymphoproliferative disorder of natural killer cells (CLPD-NK) is a subset of large granular lymphocyte (LGL) leukemia included as a provisional entity in the 2016 WHO classification of lymphoid neoplasms, which is characterized by clonal expansion of NK cells with no standardized therapy. STAT3 and TET2 mutations were previously each reported in 15%–30% of cases; however, the genomic basis of the remaining cases is still unknown. Objective: We aimed to perform a comprehensive genomic and transcriptomic analysis of CLPD-NK to elucidate the genomic landscape, investigate mechanisms of pathogenesis, and improve diagnostic classification and treatment strategies. Methods: CLPD-NK samples (n=121) were examined by whole-genome, targeted, and/or whole-transcriptome sequencing. Structural modeling, G-protein signaling, β-arrestin signaling, G-protein-coupled receptor (GPCR) for CCL22, CCR4 internalization, and chemotaxis were examined. Cell line-derived xenografts were used to examine cell proliferation and phenotype. Results: Somatic mutations in the chemokine CCL22 were found in 21.5% of CLPD-NK patients but not in 2837 cases of other hematological malignancies, including T-LGL and other NK leukemias. Clinically, there was a trend to an association with cutaneous complications and/or neurological symptoms. CCL22 mutations were enriched at the highly conserved residues Leu45, Pro46, and Pro79 and were mutually exclusive of STAT3 mutations. CCL22 mutations were associated with dysregulated gene expression, similar to normal CD56bright NK cells, with enrichment for activation of cytokine signaling pathways, representing a distinct subgroup. Most CCL22 mutations were substitutions to Arg, a basic residue, and Leu45Arg was predicted to promote electrostatic interactions with CCR4-Tyr22/Glu23 acidic residues. The mutations resulted in ligand-biased CCR4 signaling, in which activation of G-protein signaling was unchanged, but recruitment of β-arrestin, which mediates receptor internalization, was impaired, which, in turn, resulted in enhanced cell chemotaxis. CCL22 mutations drove enhanced cell proliferation in vivo in hIL-15-transgenic mice and induced an expression profile resembling that of primary samples, indicating the importance of microenvironmental crosstalk in pathogenesis. Conclusions: Somatic CCL22 mutations defined a new subgroup of CLPD-NK similar to CD56bright normal NK cells and represented a novel mechanism of leukemogenesis in which gain-of-function chemokine mutations drive tumorigenesis by biased GPCR signaling and dysregulation of microenvironmental crosstalk.

PURPOSE Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. METHODS We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. RESULTS We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations ( SF3B1, SRSF2, and U2AF1) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1- and SRSF2-related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia–like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. CONCLUSION Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.

Interrogating the tumor genome in its entirety by whole-genome sequencing (WGS) offers an unprecedented insight into the biology and pathogenesis of cancer, with potential impact on diagnostics, prognostication and therapy selection. WGS is able to detect sequence as well as structural variants and thereby combines central domains of cytogenetics and molecular genetics. Given the potential of WGS in directing targeted therapeutics and clinical decision-making, we envision a gradual transition of the method from research to clinical routine. This review is one out of three within this issue aimed at facilitating this effort, by discussing in-depth analytical validation, clinical interpretation and clinical utility of WGS. The review highlights the requirements for implementing, validating and maintaining a clinical WGS pipeline to obtain high-quality patient-specific data in accordance with the local regulatory landscape. Every step of the WGS pipeline, which includes DNA extraction, library preparation, sequencing, bioinformatics analysis, and data storage, is considered with respect to its logistics, necessities, potential pitfalls, and the required quality management. WGS is likely to drive clinical diagnostics and patient care forward, if requirements and challenges of the technique are recognized and met.

Precision diagnostics is one of the two pillars of precision medicine. Sequencing efforts in the past decade have firmly established cancer as a primarily genetically driven disease. This concept is supported by therapeutic successes aimed at particular pathways that are perturbed by specific driver mutations in protein-coding domains and reflected in three recent FDA tissue agnostic cancer drug approvals. In addition, there is increasing evidence from studies that interrogate the entire genome by whole-genome sequencing that acquired global and complex genomic aberrations including those in non-coding regions of the genome might also reflect clinical outcome. After addressing technical, logistical, financial and ethical challenges, national initiatives now aim to introduce clinical whole-genome sequencing into real-world diagnostics as a rational and potentially cost-effective tool for response prediction in cancer and to identify patients who would benefit most from 'expensive' targeted therapies and recruitment into clinical trials. However, so far, this has not been accompanied by a systematic and prospective evaluation of the clinical utility of whole-genome sequencing within clinical trials of uniformly treated patients of defined clinical outcome. This approach would also greatly facilitate novel predictive biomarker discovery and validation, ultimately reducing size and duration of clinical trials and cost of drug development. This manuscript is the third in a series of three to review and critically appraise the potential and challenges of clinical whole-genome sequencing in solid tumors and hematological malignancies.

© 2021 The Author(s)., The potential prognostic value of conventional karyotyping in adult T-cell acute lymphoblastic leukemia (T-ALL) remains an open question. We hypothesized that a modified cytogenetic classification, based on the number and type of cytogenetic abnormalities, would allow the identification of high-risk adult T-ALL patients. Complex karyotype defined by the presence of ≥3 cytogenetic alterations identified T-ALL patients with poor prognosis in this study. Karyotypes with ≥3 abnormalities accounted for 16 % (22/139) of all evaluable karyotypes, corresponding to the largest poor prognosis cytogenetic subgroup of T-ALL identified so far. Patients carrying karyotypes with ≥3 cytogenetic alterations showed a significantly inferior response to therapy, and a poor outcome in terms of event-free survival (EFS), overall survival (OS) and cumulative incidence of relapse (CIR), independently of other baseline characteristics and the end-induction minimal residual disease (MRD) level. Additional molecular analyses of patients carrying ≥3 cytogenetic alterations showed a unique molecular profile that could contribute to understand the underlying molecular mechanisms of resistance and to evaluate novel targeted therapies (e.g. IL7R directed) with potential impact on outcome of adult T-ALL patients., This project was supported by the AECC (GC16173697BIGA); ISCIII (PI19/01828) co-funded by ERDF/ESF "A way to make Europe"/ "Investing in your future", CERCA/Generalitat de Catalunya SGR 2017 288 (GRC)/ “La Caixa” P. Barba was supported by the Instituto de Salud Carlos III FIS16/01433 and PERIS 2018-2020 from Generalitat de Catalunya (BDNS357800).

Mastocytosis is a rare myeloid neoplasm characterized by uncontrolled expansion of mast cells, driven in >80% of affected individuals by acquisition of the KIT D816V mutation. To explore the hypothesis that inherited variation predisposes to mastocytosis, we performed a two-stage genome-wide association study, analyzing 1,035 individuals with KIT D816V positive disease and 17,960 healthy control individuals from five European populations. After quality control, we tested 592,007 SNPs at stage 1 and 75 SNPs at stage 2 for association by using logistic regression and performed a fixed effects meta-analysis to combine evidence across the two stages. From the meta-analysis, we identified three intergenic SNPs associated with mastocytosis that achieved genome-wide significance without heterogeneity between cohorts: rs4616402 (pmeta = 1.37 × 10−15, OR = 1.52), rs4662380 (pmeta = 2.11 × 10−12, OR = 1.46), and rs13077541 (pmeta = 2.10 × 10−9, OR = 1.33). Expression quantitative trait analyses demonstrated that rs4616402 is associated with the expression of CEBPA (peQTL = 2.3 × 10−14), a gene encoding a transcription factor known to play a critical role in myelopoiesis. The role of the other two SNPs is less clear: rs4662380 is associated with expression of the long non-coding RNA gene TEX41 (peQTL = 2.55 × 10−11), whereas rs13077541 is associated with the expression of TBL1XR1, which encodes transducin (β)-like 1 X-linked receptor 1 (peQTL = 5.70 × 10−8). In individuals with available data and non-advanced disease, rs4616402 was associated with age at presentation (p = 0.009; beta = 4.41; n = 422). Additional focused analysis identified suggestive associations between mastocytosis and genetic variation at TERT, TPSAB1/TPSB2, and IL13. These findings demonstrate that multiple germline variants predispose to KIT D816V positive mastocytosis and provide novel avenues for functional investigation., A full list of the investigators who contributed to the generation of the WTCCC data is available from the WTCCC website, funding for which was provided by The Wellcome Trust under award 07611. The KORA study was initiated and financed by the Helmholtz Zentrum München—German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. The KORA Study Group consists of A. Peters (speaker), J. Heinrich, R. Holle, R. Leidl, C. Meisinger, K. Strauch, and their co-workers, who are responsible for the design and conduct of the KORA studies. We gratefully acknowledge the contribution of all members of field staff conducting the KORA study, and we are grateful to all study participants of KORA for their invaluable contributions to this study. The InCHIANTI study baseline (1998–2000) was supported as a “targeted project” (ICS110.1/RF97.71) by the Italian Ministry of Health and in part by the U.S. National Institute on Aging (263 MD 9164 and 263 MD 821336). The Spanish mastocytosis cohort and the Spanish National DNA Bank were supported by grants from the Instituto de Salud Carlos III and FEDER (PI16/00642 and PT17/0015/0044). The Italian cohort of mastocytosis-affected individuals was funded by the Associazione Italiana per la Ricerca sul cancro, Mynerva project, 21267. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation. A.A.Z.D. was supported by a Lady Tata International Award; G.G., N.C.P.C., Y.H., A.J.C., and W.J.T. were supported by Blood Cancer UK (13002 and 18007).

Nucleophosmin (NPM1) mutations in acute myeloid leukemia (AML) affect exon 12, but also sporadically affect exons 9 and 11, causing changes at the protein C-terminal end (tryptophan loss, nuclear export signal [NES] motif creation) that lead to aberrant cytoplasmic NPM1 (NPM1c+), detectable by immunohistochemistry. Combining immunohistochemistry and molecular analyses in 929 patients with AML, we found non–exon 12 NPM1 mutations in 5 (1.3%) of 387 NPM1c+ cases. Besides mutations in exons 9 (n = 1) and 11 (n = 1), novel exon 5 mutations were discovered (n = 3). Another exon 5 mutation was identified in an additional 141 patients with AML selected for wild-type NPM1 exon 12. Three NPM1 rearrangements (NPM1/RPP30, NPM1/SETBP1, NPM1/CCDC28A) were detected and characterized among 13 979 AML samples screened by cytogenetic/fluorescence in situ hybridization and RNA sequencing. Functional studies demonstrated that in AML cases, new NPM1 proteins harbored an efficient extra NES, either newly created or already present in the fusion partner, ensuring its cytoplasmic accumulation. Our findings support NPM1 cytoplasmic relocation as critical for leukemogenesis and reinforce the role of immunohistochemistry in predicting AML-associated NPM1 genetic lesions. This study highlights the need to develop new assays for molecular diagnosis and monitoring of NPM1-mutated AML.

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with increased risk of cancers and inflammation-related diseases. This phenomenon becomes common in persons aged ≥80 years, in whom the implications of CHIP are not well defined. We performed a mutational screening in 1794 persons aged ≥80 years and investigated the relationships between CHIP and associated pathologies. Mutations were observed in one-third of persons aged ≥80 years and were associated with reduced survival. Mutations in JAK2 and splicing genes, multiple mutations (DNMT3A, TET2, and ASXL1 with additional genetic lesions), and variant allele frequency ≥0.096 had positive predictive value for myeloid neoplasms. Combining mutation profiles with abnormalities in red blood cell indices improved the ability of myeloid neoplasm prediction. On this basis, we defined a predictive model that identifies 3 risk groups with different probabilities of developing myeloid neoplasms. Mutations in DNMT3A, TET2, ASXL1, or JAK2 were associated with coronary heart disease and rheumatoid arthritis. Cytopenia was common in persons aged ≥80 years, with the underlying cause remaining unexplained in 30% of cases. Among individuals with unexplained cytopenia, the presence of highly specific mutation patterns was associated with myelodysplastic-like phenotype and a probability of survival comparable to that of myeloid neoplasms. Accordingly, 7.5% of subjects aged ≥80 years with cytopenia had presumptive evidence of myeloid neoplasm. In summary, specific mutational patterns define different risk of developing myeloid neoplasms vs inflammatory-associated diseases in persons aged ≥80 years. In individuals with unexplained cytopenia, mutational status may identify those subjects with presumptive evidence of myeloid neoplasms.

Fusion transcripts are frequent genetic abnormalities in myeloid malignancies and are often the basis for risk stratification, minimal residual disease (MRD) monitoring, and targeted therapy. We comprehensively analyzed the fusion transcript landscape in 572 acute myeloid leukemia (AML) and 630 myelodysplastic syndrome (MDS) patients by whole transcriptome sequencing (WTS). Totally, 274 fusion events (131 unique fusions) were identified in 210/572 AML patients (37%). In 16/630 MDS patients, 16 fusion events (15 unique fusions) were detected (3%). In AML, 141 cases comprised entity-defining rearrangements (51% of all detected fusions) and 21 (8%) additional well-known fusions, all detected by WTS (control group). In MDS, only 1 fusion was described previously (NRIP1-MECOM, n = 2). Interestingly, a high number of so-far unreported fusions were found (41% [112/274] in AML, 88% [14/16] in MDS), all validated by cytogenetic and/or whole genome sequencing data. With 1 exception (CTDSP1-CFLAR, n = 2), all novel fusions were observed in 1 patient each. In AML, cases with novel fusions showed concomitantly a high frequency of TP53 mutations (67%) and of a complex karyotype (71%), which was also observed in MDS, but less pronounced (TP53, 26%; complex karyotype, 21%). A functional annotation of genes involved in novel fusions revealed many functional relevant genes (eg, transcription factors; n = 28 in AML, n = 2 in MDS) or enzymes (n = 42 in AML, n = 9 in MDS). Taken together, new genomic alterations leading to fusion transcripts were much more common in AML than in MDS. Any novel fusions might be of use for developing markers (eg, for MRD monitoring), particularly in cases without an entity-defining abnormality.

B cell maturation antigen (BCMA) is a target for various immunotherapies and a biomarker for tumor load in multiple myeloma (MM). We report a case of irreversible BCMA loss in a patient with MM who was enrolled in the KarMMa trial ( NCT03361748 ) and progressed after anti-BCMA CAR T cell therapy. We identified selection of a clone with homozygous deletion of TNFRSF17 (BCMA) as the underlying mechanism of immune escape. Furthermore, we found heterozygous TNFRSF17 loss or monosomy 16 in 37 out of 168 patients with MM, including 28 out of 33 patients with hyperhaploid MM who had not been previously treated with BCMA-targeting therapies, suggesting that heterozygous TNFRSF17 deletion at baseline could theoretically be a risk factor for BCMA loss after immunotherapy. Biallelic loss of BCMA caused a patient with multiple myeloma to relapse after anti-BCMA CAR T cell treatment. Baseline heterozygous BCMA deletions might be a risk factor for this form of resistance.

Molecular diagnostics as the centrepiece of precision oncology has gone through revolutionary developments over the last decade, becoming tremendously broad, deep and precise with still ongoing advancements. In the majority of scenarios, treatment selection for cancer patients without any type of molecular characterization is no longer conceivable. Considering the impact of sample quality on the reliability of molecular analyses and the importance of the results for the fate of an individual patient, it is surprising how sparsely preanalytical and analytical requirements are addressed scientifically. Standardization and rigorous quality assessment continue to play only a marginal role in the field. Within this review, we will systematically discuss influencing preanalytic parameters and technology setups affecting molecular test results. We will shed light on the specifics of different analytes, technical modalities, and analysis pipelines. The review will have a certain focus on broad molecular genetic tumour testing with next generation sequencing but will go beyond that including other molecular diagnostic modalities and will give a glimpse into the future of molecular testing.

Mixed-lineage leukemia (MLL) gene rearrangements are among the most frequent chromosomal abnormalities in acute myeloid leukemia (AML). MLL fusion patterns are associated with the patient’s prognosis; however, their relationship with driver mutations is unclear. We conducted sequence analyses of 338 genes in pediatric patients with MLL-rearranged (MLL-r) AML (n = 56; JPLSG AML-05 study) alongside data from the TARGET study’s pediatric cohorts with MLL-r AML (n = 104), non–MLL-r AML (n = 581), and adult MLL-r AML (n = 81). KRAS mutations were most frequent in pediatric patients with high-risk MLL fusions (MLL-MLLLT10, MLL-MLLT4, and MLL-MLLT1). Pediatric patients with MLL-r AML (n = 160) and a KRAS mutation (KRAS-MT) had a significantly worse prognosis than those without a KRAS mutation (KRAS-WT) (5-year event-free survival [EFS]: 51.8% vs 18.3%, P < .0001; 5-year overall survival [OS]: 67.3% vs 44.3%, P = .003). The adverse prognostic impact of KRAS mutations was confirmed in adult MLL-r AML. KRAS mutations were associated with adverse prognoses in pediatric patients with both high-risk (MLLT10+MLLT4+MLLT1; n = 60) and intermediate-to-low–risk (MLLT3+ELL+others; n = 100) MLL fusions. The prognosis did not differ significantly between patients with non–MLL-r AML with KRAS-WT or KRAS-MT. Multivariate analysis showed the presence of a KRAS mutation to be an independent prognostic factor for EFS (hazard ratio [HR], 2.21; 95% confidence interval [CI], 1.35-3.59; P = .002) and OS (HR, 1.85; 95% CI, 1.01-3.31; P = .045) in MLL-r AML. The mutation is a distinct adverse prognostic factor in MLL-r AML, regardless of risk subgroup, and is potentially useful for accurate treatment stratification. This trial was registered at the UMIN (University Hospital Medical Information Network) Clinical Trials Registry (UMIN-CTR; http://www.umin.ac.jp/ctr/index.htm) as #UMIN000000511., 急性骨髄性白血病の予後を予測する新規マーカーを発見 --リスクに応じた適切な治療につながる可能性--. 京都大学プレスリリース. 2020-10-02.

The 2016 revision of the World Health Organization classification of tumors of hematopoietic and lymphoid tissues is characterized by a closer integration of morphology and molecular genetics. Notwithstanding, the myelodysplastic syndrome (MDS) with isolated del(5q) remains so far the only MDS subtype defined by a genetic abnormality. Approximately half of MDS patients carry somatic mutations in spliceosome genes, with SF3B1 being the most commonly mutated one. SF3B1 mutation identifies a condition characterized by ring sideroblasts (RS), ineffective erythropoiesis, and indolent clinical course. A large body of evidence supports recognition of SF3B1-mutant MDS as a distinct nosologic entity. To further validate this notion, we interrogated the data set of the International Working Group for the Prognosis of MDS (IWG-PM). Based on the findings of our analyses, we propose the following diagnostic criteria for SF3B1-mutant MDS: (1) cytopenia as defined by standard hematologic values, (2) somatic SF3B1 mutation, (3) morphologic dysplasia (with or without RS), and (4) bone marrow blasts

Part of this work was presented as abstract to the 60th annual meeting of the American Society of Hematology, San Diego, CA, December 2018 (Abstract # 4 4 42)., The diagnosis and risk stratification of multiple myeloma (MM) is based on clinical and cytogenetic tests. Magnetic CD138 enrichment followed by interphase FISH (fluorescence in situ hybridisation) is the gold standard to identify prognostic translocations and copy number alterations (CNA). Although clinical implications of gene expression profiling (GEP) or panel based sequencing results are evident, those tests have not yet reached routine clinical application. We set up a single workflow to analyse MM of 211 patients at first diagnosis by whole genome sequencing (WGS) and RNA-Seq and validate the results by FISH analysis. We observed a 96% concordance of FISH and WGS results when assessing translocations involving the IGH locus and an overall concordance of FISH and WGS of 92% when assessing CNA. WGS analysis resulted in the identification of 17 additional MYC-translocations that were missed by FISH analysis. RNA-Seq followed by supervised clustering grouped patients in their expected genetically defined subgroup and prompted the assessment of WGS data in cases that were not congruent with FISH. This allowed the identification of additional IGH-translocations and hyperdiploid cases. We show the reliability of WGS an RNA-Seq in a clinical setting, which is a prerequisite for a novel routine diagnostic test., JMHS was sponsered by a grant from Torsten Haferlach Leukämiediagnostikstiftung.

Background: Clonal hematopoiesis of indeterminate potential (CHIP) is associated with increased risk of cancers and inflammation-related diseases. The phenomenon becomes very common in oldest-old individuals, in whom the implications of CHIP are not well defined. Here we investigate the relationships between CHIP and associated pathologies in people aged >80 years. Methods: We performed a mutational screening in 1794 oldest-old individuals enrolled in two population-based studies (“Health_&_Anemia” and “Monzino_80+”). Findings: Clonal mutations were observed in one third of oldest-old individuals and were associated with reduced survival and increased risk of cancers. Mutations in JAK2 and splicing genes (SF3B1, SRSF2, U2AF1, ZRSR2), multiple mutations (DNMT3A, TET2, ASXL1) combined with additional genetic lesions) and variant allele frequency ≥0.14 had positive predictive value for myeloid neoplasms. Combining mutation profiles with abnormalities in red blood cell indices improved the ability of myeloid neoplasm prediction. On this basis, we defined a predictive model that identifies 3 risk groups with different probabilities of developing myeloid neoplasms. Mutations in DNMT3A, TET2, ASXL1 or JAK2 (most occurring as single lesion) were associated with coronary heart disease and rheumatoid arthritis. Cytopenia (most including anemia) was a common finding in oldest-old population (>20%), the underlying cause remaining unexplained in 30% of cases. Among individuals with unexplained cytopenia, the presence of highly-specific mutation patterns was associated with a myeloid neoplasms-like phenotype and a probability of survival comparable to that of myeloid neoplasms. Accordingly, 7.5% of oldest-old subjects with cytopenia had presumptive evidence of myeloid neoplasm. Interpretation: Specific mutational patterns define different risk of developing cancers vs. inflammatory-associated diseases in oldest-old population. In individuals with unexplained cytopenia, mutational status may identify those subjects with presumptive evidence of myeloid neoplasms. Trial Registration: (ClinicalTrials.gov number:NCT03907553) Funding: Supported by European Union; Cariplo Foundation, Italy; Italian Association for Cancer Research; Italian Ministry of Health and Research. Declaration of Interests: The Authors declare no competing interests. Ethics Approval Statement: Study procedures were in accordance with the Declaration of Helsinki. Ethics Committees of Humanitas Research Hospital and Mario Negri Pharmacological Institute, Milan Italy approved the study. Written informed consent was obtained prior to blood sampling.

Chromosomal translocation t(8;21)(q22;q22) which leads to the generation of oncogenic RUNX1-RUNX1T1 (AML1-ETO) fusion is observed in approximately 10% of acute myelogenous leukemia (AML). To identify somatic mutations that co-operate with t(8;21)-driven leukemia, we performed whole and targeted exome sequencing of an Asian cohort at diagnosis and relapse. We identified high frequency of truncating alterations in ASXL2 along with recurrent mutations of KIT, TET2, MGA, FLT3, and DHX15 in this subtype of AML. To investigate in depth the role of ASXL2 in normal hematopoiesis, we utilized a mouse model of ASXL2 deficiency. Loss of ASXL2 caused progressive hematopoietic defects characterized by myeloid hyperplasia, splenomegaly, extramedullary hematopoiesis, and poor reconstitution ability in transplantation models. Parallel analyses of young and >1-year old Asxl2-deficient mice revealed age-dependent perturbations affecting, not only myeloid and erythroid differentiation, but also maturation of lymphoid cells. Overall, these findings establish a critical role for ASXL2 in maintaining steady state hematopoiesis, and provide insights into how its loss primes the expansion of myeloid cells.

Azacitidine is the first drug to demonstrate a survival benefit for patients with MDS. However, only half of patients respond and almost all patients eventually relapse. Limited and conflicting data are available on predictive factors influencing response. We analyzed 128 patients from two institutions with MDS or AML treated with azacitidine to identify prognostic indicators. Genetic mutations in ASXL1, RUNX1, DNMT3A, IDH1, IDH2, TET2, TP53, NRAS, KRAS, FLT3, KMT2A-PTD, EZH2, SF3B1, and SRSF2 were assessed by next-generation sequencing. With a median follow up of 5.6 years median survival was 1.3 years with a response rate of 49%. The only variable with significant influence on response was del(20q). All 6 patients responded (p = 0.012) but survival was not improved. No other clinical, cytogenetic or molecular marker for response or survival was identified. Interestingly, patients from poor-risk groups as high-risk cytogenetics (55%), t-MDS/AML (54%), TP53 mutated (48%) or relapsed after chemotherapy (60%) showed a high response rate. Factors associated with shorter survival were low platelets, AML vs. MDS, therapy-related disease, TP53 and KMT2A-PTD. In multivariate analysis anemia, platelets, FLT3-ITD, and therapy-related disease remained in the model. Poor-risk factors such as del(7q)/-7, complex karyotype, ASXL1, RUNX1, EZH2, and TP53 did not show an independent impact. Thus, no clear biomarker for response and survival can be identified. Although a number of publications on predictive markers for response to AZA exist, results are inconsistent and improved response rates did not translate to improved survival. Here, we provide a comprehensive overview comparing the studies published to date.

RUNX1 (runt-related transcription factor 1) is a myeloid transcription factor widely expressed in hematopoietic cells that regulates the differentiation into mature blood cells [1,2]. Mutations in ...

Deletions in the long arm of chromosome 9, del(9q), are recurrent but rare cytogenetic aberrations in myeloid neoplasms. In patients with acute myeloid leukemia (AML), del(9q) are observed in ∼2% o...

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. Figure 1 Figure 1. Disclosures Haferlach: 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.

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. Figure 1 Figure 1. Disclosures Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

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. Figure 1 Figure 1. Disclosures Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Background: Paroxysmal nocturnal hemoglobinuria (PNH) is a hemolytic anemia associated with severe thrombophilia and characterized by complement-mediated lysis of erythrocytes lacking glycosylphosphatidylinositol (GPI)-anchored proteins. In the majority of cases, GPI deficiency is caused by somatic mutations in the PIGA gene. Presence of PNH clones is associated with acquired aplastic anemia (AA) and can be found in patients with myelodysplastic syndrome (MDS) or rarely other myeloid neoplasms (MN). Flow cytometric analysis for deficiency of GPI-anchored proteins on multiple cell lineages detects PNH clones, and PIGA mutational analysis is not mandatory to establish the diagnosis. In contrast, molecular genetic analysis of targeted gene panels is widely used in the diagnostic workup of MN. We hypothesized that the inclusion of PIGA into the myeloid gene panel could identify obscure cases with PNH clones irrespective of the initial clinical suspicion. Aim: To assess the significance of incidental findings of mutations in PIGA in the diagnostic workup of MN. Methods: 20,320 consecutive patients undergoing sequencing analysis for a confirmed or suspected MN were analyzed for the presence of mutations in the PIGA gene. Patients with confirmed PNH analyzed only for PIGA were not included, and cases with previously known PIGA mutations were excluded from further analysis. DNA was isolated from peripheral blood (PB) or bone marrow, and sequencing was performed on NovaSeq after Illumina DNA Prep for Enrichment library preparation (Illumina, San Diego, CA) and hybrid capture of a 41 gene panel including the complete coding sequence of PIGA (IDT Inc., Coralville, IA); data was analyzed with Pisces and Pindel (BaseSpace, Illumina). Flow cytometry was performed on granulocytes, monocytes, and erythrocytes in PB using antibodies against GPI-anchored proteins (CD14, CD24, CD55, and CD59), fluorescein-labeled proaerolysin (FLAER) staining, and Navios cytometers; analysis was done using Kaluza software (both Beckman Coulter, Miami, FL). Results: PIGA mutations were newly identified in 67 patients (0.3%) undergoing targeted sequencing within the diagnostic workup of MN. 30 patients were excluded from further analysis as the gene panel had been requested for a MN associated with previously diagnosed PNH. From the remaining patients, PB for flow cytometry analysis could be obtained from 20 patients. Flow cytometry confirmed the presence of a PNH clone in 17 (85%) of these patients (median clone size: 41% for granulocytes, 54.5% for monocytes, and 12% for erythrocytes). In 3 patients (15%) with unexpected PIGA mutations, flow cytometry detected no PNH clone. The type of PIGA mutations differed significantly in those cases: Patients in whom a PNH clone was confirmed, showed protein-truncating frame-shift (41%) or nonsense (6%) mutations, splice site mutations (18%), or multiple mutations (35%) including at least one protein-truncating mutation at a median variant allele frequency (VAF) of 15.4% (range 2.0% to 50.1%). In contrast, patients without a PNH clone showed only singular missense mutations of PIGA with a VAF of 3.6% to 5.3% (Figure 1). Final diagnoses in patients with confirmed clones were sole PNH (n=9), or PNH clone associated with MDS (n=4), AA (n=3), and AML (n=1), and additional mutations in other genes were observed in 9 cases. While the initial clinical presentation included the differential diagnosis of PNH in some of the patients, flow cytometry was requested as a direct result of the PIGA mutation in 4 cases with an accompanying MN and in 3 patients without - the later showing a median latency of 6.5 years from the initial clinical presentation to the diagnosis. Con clusions: The inclusion of PIGA into a standardized targeted sequencing panel for MN helps to identify patients with PNH clones irrespective of the initial clinical suspicion but is not sufficient to rule out PNH. Protein-truncating PIGA mutations are highly specific for PNH clones whereas singular missense mutations may not necessarily effect GPI biosynthesis. Our data indicate that the incidental finding of a PIGA mutation in sequencing analysis shall entail flow cytometry of GPI-anchored proteins in PB. The potential clinical sequelae and the availability of specific treatment options such as complement inhibitors warrant the thorough exclusion of PNH in the diagnostic workup of suspected MN. Figure 1 Figure 1. Disclosures Hoermann: Novartis: Honoraria. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership.

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 Figure 1 Figure 1. Disclosures Kern: 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.

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. Figure 1 Figure 1. Disclosures Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

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 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 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). Figure 1 Figure 1. Disclosures Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

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 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 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. Figure 1 Figure 1. Disclosures Diez-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.

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 ( 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. Figure 1 Figure 1. Disclosures Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership.