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Transcriptome sequencing has identified multiple subtypes of B-progenitor acute lymphoblastic leukemia (B-ALL) of prognostic significance, but a minority of cases lack a known genetic driver. Here, we used integrated whole-genome (WGS) and -transcriptome sequencing (RNA-seq), enhancer mapping, and chromatin topology analysis to identify previously unrecognized genomic drivers in B-ALL. Newly diagnosed (n = 3221) and relapsed (n = 177) B-ALL cases with tumor RNA-seq were studied. WGS was performed to detect mutations, structural variants, and copy number alterations. Integrated analysis of histone 3 lysine 27 acetylation and chromatin looping was performed using HiChIP. We identified a subset of 17 newly diagnosed and 5 relapsed B-ALL cases with a distinct gene expression profile and 2 universal and unique genomic alterations resulting from aberrant recombination-activating gene activation: a focal deletion downstream of PAN3 at 13q12.2 resulting in CDX2 deregulation by the PAN3 enhancer and a focal deletion of exons 18-21 of UBTF at 17q21.31 resulting in a chimeric fusion, UBTF::ATXN7L3. A subset of cases also had rearrangement and increased expression of the PAX5 gene, which is otherwise uncommon in B-ALL. Patients were more commonly female and young adult with median age 35 (range,12-70 years). The immunophenotype was characterized by CD10 negativity and immunoglobulin M positivity. Among 16 patients with known clinical response, 9 (56.3%) had high-risk features including relapse (n = 4) or minimal residual disease >1% at the end of remission induction (n = 5). CDX2-deregulated, UBTF::ATXN7L3 rearranged (CDX2/UBTF) B-ALL is a high-risk subtype of leukemia in young adults for which novel therapeutic approaches are required.

Among the most common genetic alterations in the myelodysplastic syndromes (MDS) are mutations in the spliceosome gene SF3B1. Such mutations induce specific RNA missplicing events, directly promote ring sideroblast (RS) formation, generally associate with more favorable prognosis, and serve as a predictive biomarker of response to luspatercept. However, not all SF3B1 mutations are the same, and here we report that the E592K variant of SF3B1 associates with high-risk disease features in MDS, including a lack of RS, increased myeloblasts, a distinct co-mutation pattern, and decreased survival. Moreover, in contrast to canonical SF3B1 mutations, E592K induces a unique RNA missplicing pattern, retains an interaction with the splicing factor SUGP1, and preserves normal RNA splicing of the sideroblastic anemia genes TMEM14C and ABCB7. These data expand our knowledge of the functional diversity of spliceosome mutations, and they suggest that patients with E592K should be approached differently from low-risk, luspatercept-responsive MDS patients with ring sideroblasts and canonical SF3B1 mutations.

B cell maturation antigen (BCMA) target loss is considered to be a rare event that mediates multiple myeloma (MM) resistance to anti-BCMA chimeric antigen receptor T cell (CAR T) or bispecific T cell engager (TCE) therapies. Emerging data report that downregulation of G protein coupled receptor family C group 5 member D (GPRC5D) protein often occurs at relapse after anti-GPRC5D CAR T. To examine the tumor intrinsic factors that promote MM antigen escape, we performed combined bulk and single cell whole genome sequencing/ copy number variation analysis of 25 patients treated with anti-BCMA and/ or -GPRC5D CAR T/ TCE. In two cases, MM relapse post TCE/CAR T was driven by BCMA negative clones harboring focal biallelic deletions at the TNFRSF17 locus at relapse or by selective expansion of pre-existing subclones with biallelic TNFRSF17 loss. In another three cases of MM relapse, we identified non-truncating missense mutations or in-frame deletions in the extracellular domain of BCMA which negates the efficacies of anti-BCMA TCEs, despite detectable surface BCMA protein expression. With respect to GPRC5D, we report the first four cases of MM relapse with biallelic mutations of GPRC5D following anti-GPRC5D TCE, including two cases with convergent evolution where multiple subclones lost GPRC5D through different somatic events. Our data support that immunoselection of BCMA or GPRC5D negative or mutant clones post CAR T/ TCE therapies may be more prevalent than currently perceived. The engineering and selection of immunotherapies in MM should account for target antigen structural and extracellular domain mutations.

Current classifications (WHO-HAEM5 / ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes, which are defined by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by RNA-Seq is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning based classifier using RNA-Seq expression data to allocate BCP-ALL samples to 21 defined molecular subtypes. Trained on n=1,869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n=1,018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). ‘High confidence predictions’ were achieved in 84.6% of samples with 99.7% accuracy. Only 1.2% of samples remained ‘unclassified’. ALLCatchR outperformed existing tools and identified novel candidates in previously unassigned samples. We established a novel RNA-Seq reference of human B-lymphopoiesis. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory, which identified shared patterns of proximity of BCP-ALL subtypes to normal lymphopoiesis stages. ALLCatchR sustains RNA-Seq routine application in BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocations and novel insights into underlying developmental trajectories.

Key Points Comparison of the mutation frequencies and numbers of 122 genes in 3096 cases enables identification of “mutation-driven” entities.Differences in mutation patterns in cases with or without CHIP-associated mutations across entities suggest differences in pathophysiology., Visual Abstract, Acquired somatic mutations are crucial for the development of most cancers. We performed a comprehensive comparative analysis of the mutational landscapes and their correlation with CHIP-related (clonal hematopoiesis of indeterminate potential) mutations and patient age of 122 genes in 3096 cases of 28 different hematological malignancies. Differences were observed regarding (1) the median number of mutations (highest, median n = 4; lowest, n = 0); (2) specificity of certain mutations (high frequencies in atypical chronic myeloid leukemia [aCML; ASXL1, 86%], follicular lymphoma [FL; KMT2D, 87%; CREBBP, 73%], hairy cell lymphoma [BRAF, 100%], lymphoplasmacytic lymphoma [MYD88, 98%; CXCR4, 51%], myeloproliferative neoplasm [MPN; AK2, 68%]); (3) distribution of mutations (broad distribution within/across the myeloid/lymphoid lineage for TET2, ASXL1, DNMT3A, TP53, BCOR, and ETV6); (4) correlation of mutations with patient’s age (correlated with older age across entities: TET2, DNMT3A, ASXL1, TP53, EZH2, BCOR, GATA2, and IDH2; younger age: KIT, POT1, RAD21, U2AF2, and WT1); (5) correlation of mutation number per patient with age. Moreover, we observed high frequencies of mutations in RUNX1, SRSF2, IDH2, NRAS, and EZH2 in cases comprising at least 1 DTA (DNMT3A, TET2, ASXL1) mutation, whereas in cases without DTA mutations, TP53, KRAS, WT1, and SF3B1 were more frequent across entities, suggesting differences in pathophysiology. These results give further insight into the complex genetic landscape and the role of DTA mutations in hematological neoplasms and define mutation-driven entities (myelodysplastic syndrome/MPN overlap; secondary acute myeloid) in comparison with entities defined by chromosomal fusions (chronic myeloid leukemia; myeloid/lymphoid neoplasm with eosinophilia).

T cell–engaging immunotherapies exert unprecedented single-agent activity in multiple myeloma (MM), thereby putting a yet unexplored selective pressure on the clonal architecture. In this study, we report on homozygous BCMA (TNFRSF17) gene deletion after BCMA-targeting T cell–redirecting bispecific antibody therapy in a heavily pretreated MM patient. Loss of BCMA protein expression persisted over subsequent relapses, with no response to treatment with anti-BCMA antibody drug conjugate. In light of the multiple alternative targets that are emerging in addition to BCMA, we extended our analyses to delineate a more complete picture of genetic alterations that may have an impact on immunotherapy targets in MM. We performed whole-genome sequencing and RNA sequencing in 100 MM patients (50 were newly diagnosed; 50 were relapsed/refractory) and identified a significant proportion of patients with aberrations in genes encoding immunotherapy targets; GPRC5D ranked first with 15% heterozygous deletions, followed by CD38 (10%), SDC1 (5%), and TNFRSF17 (4%). Notably, these heterozygous deletions did not lower the expression levels of respective genes, but they may represent a first hit that drives the acquisition of homozygous deletions and subsequent antigen-loss relapse upon targeted immunotherapy. In summary, we show preexisting vulnerability in genes encoding immunotargets before and homozygous deletions after T cell–engaging immunotherapy.

MYC rearrangements (MYCr) occur in several B-cell neoplasms and impact disease progression and overall survival. We used whole genome sequencing (WGS) and whole transcriptome sequencing (WTS) to analyze and compare MYCr in different B-cell neoplasms. The MYCr features of cases with plasma cell myeloma (PCM) (n = 88) showed distinct characteristics compared to cases with mature B-cell lymphomas (n = 62, including Burkitt lymphoma (BL), diffuse large B-cell lymphoma (DLBCL) and high grade lymphoma with MYC and BCL2 and/or BCL6 rearrangements (HGBL)): they were more complex and showed a wider variety of translocation partners and breakpoints. Additionally, unlike B-cell lymphomas, they showed no evidence of activation-induced deaminase (AID) involvement in the formation of MYCr with immunoglobolin heavy chain (IGH), indicating a different mechanism of origin. The different MYCr characteristics resulted in poor MYCr detection rates by fluorescence in situ hybridization of only 50% in PCM, compared to 94% in lymphoma.

Artificial intelligence (AI) is about to make itself indispensable in the health care sector. Examples of successful applications or promising approaches range from the application of pattern recognition software to pre-process and analyze digital medical images, to deep learning algorithms for subtype or disease classification, and digital twin technology and in silico clinical trials. Moreover, machine-learning techniques are used to identify patterns and anomalies in electronic health records and to perform ad-hoc evaluations of gathered data from wearable health tracking devices for deep longitudinal phenotyping. In the last years, substantial progress has been made in automated image classification, reaching even superhuman level in some instances. Despite the increasing awareness of the importance of the genetic context, the diagnosis in hematology is still mainly based on the evaluation of the phenotype. Either by the analysis of microscopic images of cells in cytomorphology or by the analysis of cell populations in bidimensional plots obtained by flow cytometry. Here, AI algorithms not only spot details that might escape the human eye, but might also identify entirely new ways of interpreting these images. With the introduction of high-throughput next-generation sequencing in molecular genetics, the amount of available information is increasing exponentially, priming the field for the application of machine learning approaches. The goal of all the approaches is to allow personalized and informed interventions, to enhance treatment success, to improve the timeliness and accuracy of diagnoses, and to minimize technically induced misclassifications. The potential of AI-based applications is virtually endless but where do we stand in hematology and how far can we go?

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a very rare but highly aggressive hematologic malignancy derived from precursors of plasmacytoid dendritic cells. Despite initial response to...

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 1,258 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.

The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve the integration of artificial intelligence (AI)-based systems into routine practice to support the hematologists' decision making. Several studies have shown that AI-based models can already be used to automatically differentiate cells, reliably detect malignant cell populations, support chromosome banding analysis, and interpret clinical variants, contributing to early disease detection and prognosis. However, even the best tool can become useless if it is misapplied or the results are misinterpreted. Therefore, in order to comprehensively judge and correctly apply newly developed AI-based systems, the hematologist must have a basic understanding of the general concepts of machine learning. In this review, we provide the hematologist with a comprehensive overview of various machine learning techniques, their current implementations and approaches in different diagnostic subfields (e.g., cytogenetics, molecular genetics), and the limitations and unresolved challenges of the systems.

Myelodysplastic syndromes (MDS) comprise a heterogeneous group of diseases classified by comprehensive diagnostics. Identification of homogeneous subgroups is desirable to understand differences in clinical course and to develop targeted treatment approaches. We identified a specific CD11b/CD16 expression pattern in granulocytes associated with reduced CD45 expression in myeloid progenitor cells (MPC) in MDS cases and assessed its genetic background by whole genome (WGS) and whole transcriptome sequencing (WTS).The cohort consisted of 32 MDS cases with the specific aberrant immunophenotype. Since all these 32 cases were found to be SRSF2 mutated additional 51 SRSF2 mutated MDS cases without this specific immunophenotype were selected as controls. For all cases WGS and WTS were performed.The immunophenotype newly identified in SRSF2 mutated MDS patients is characterized (1) by a specific maturation pattern, i.e. an increase of CD11b expression without CD16 expression followed by an increase in CD16 expression without further CD11b expression and (2) by only dim CD45 expression of MPC. STAG2 mutations were exclusively found in MDS cases with the specific immunophenotype (17/32, 53% vs. 0%, p 0.001). Hence,50% of cases with the specific immunophenotype were characterized by co-mutations in SRSF2 and STAG2. In addition, cluster analysis revealed a specific gene expression profile of such cases.We here for the first time describe a specific immunophenotype which defines MDS cases with SRSF2 mutations and a consistent and specific mutational and gene expression profile. This comprehensive data warrants analysis of further such cases to assess the feasibility of defining a new sub-entity of MDS.

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.

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.

Simple Summary In hematological malignancies, genome-wide sequencing studies found the process of splicing to be surprisingly frequently disrupted. While recent studies characterized altered splicing in relation to splicing factor mutations in AML, this study explored differential splicing profiles associated with two most common aberrations in AML: FLT3-ITD and NPM1 mutations. We identified the differential splicing of FAB-type specific gene sets in FLT3-ITD+/NPM1+ specimens as compared to FLT3-ITD−/NPM1− samples. The primary functions perturbed by differential splicing in all three FAB types included cell cycle control and DNA damage response. Interestingly, differential expression mainly affected genes involved in hematopoietic differentiation. Our findings increase our understanding of how genetic mutations translate to phenotypic features of AML cells to further improve response predictions and to find innovative therapeutic approaches. Altogether, to the best of our knowledge, this is the first study to report differential splicing profiles associated with FLT3-ITD with a concomitant NPM1 mutation in AML. Abstract Despite substantial progress achieved in unraveling the genetics of AML in the past decade, its treatment outcome has not substantially improved. Therefore, it is important to better understand how genetic mutations translate to phenotypic features of AML cells to further improve response predictions and to find innovative therapeutic approaches. In this respect, aberrant splicing is a crucial contributor to the pathogenesis of hematological malignancies. Thus far, altered splicing is well characterized in relation to splicing factor mutations in AML. However, splicing profiles associated with mutations in other genes remain largely unexplored. In this study, we explored differential splicing profiles associated with two of the most common aberrations in AML: FLT3-ITD and NPM1 mutations. Using RNA-sequencing data of a total of 382 primary AML samples, we found that the co-occurrence of FLT3-ITD and mutated NPM1 is associated with differential splicing of FAB-type specific gene sets. Despite the FAB-type specificity of particular gene sets, the primary functions perturbed by differential splicing in all three FAB types include cell cycle control and DNA damage response. Interestingly, we observed functional divergence between alternatively spliced and differentially expressed genes in FLT3-ITD+/NPM1+ samples in all analyzed FAB types, with differential expression affecting genes involved in hematopoietic differentiation. Altogether, these observations indicate that concomitant FLT3-ITD and mutated NPM1 are associated with the maturation state-specific differential splicing of genes with potential oncogenic relevance.

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.

False positivity of antigen immunoassays used as an early diagnostic tool to detect invasive fungal infections is known. Interpretation of the assay needs the identification of sources which could affect the specificity of the test. We focused on the influence of parenteral nutrition (PN) and piperacillin-tazobactam (TZP) on fungal immunoassays. Measurable amounts of Candida antigen mannan were detected in several compounds of PN and TZP in a previous in vitro study. In the current study, 84 patients undergoing allogeneic haematopoietic cell transplantation receiving either TZP, PN or both were monitored with Aspergillus and Candida antigen assay. Six patients were analysed closer in a kinetic analysis with more frequent blood sampling to detect mannan. PN in diverse compositions as well as TZP did not increase significantly the amount of mannan and the Aspergillus antigen in serum. We could not confirm the positive results of the in vitro study. Physicians should be aware that mannan antigenemia due to drug infusion could be a transient issue and should be considered in the interpretation of fungal immunoassays, although we could not find clinically relevant effects on mannan levels.