ALBERT

Description: Recurrent somatic mutations of CUX1 are described in myeloid neoplasms. CUX1 is located at chromosome 7q22.1; -7/del(7q) involving CUX1 locus are common abnormalities in myelodysplastic syndromes (MDS). Mutations and loss of heterozygosity involving CUX1 have been also described in breast, lung and uterine cancers. Preliminary functional studies, lack of a mutational hotspot and coincidental deletions suggest loss of function/hypomorphic consequences of these molecular defects. CUX1 (p200), contains 4 evolutionarily conserved DNA-binding domains, including 3 CUT repeats and a CUT homeodomain. Functionally, CUX1 regulates many genes involved in DNA replication and chromosome segregation. Cell-based assays have established a role for CUX1 in the control of cell-cycle progression, cell motility, and invasion .The objective of this study is to assess the molecular context and clinical significance of CUX1 mutations and deletions in myeloid neoplasms. We analyzed a subset of 1478 patients [24% lower-risk MDS, 17% higher-risk MDS, 22% primary (p)AML, 14% secondary AML, 14% MDS/myeloproliferative neoplasms (MPN) and 9% MPN] for the presence of CUX1 mutations and deletions. No CUX1 mutations were found in core binding factor AML. We correlated the presence of these lesions with clinical parameters, cytogenetic abnormalities, and molecular features including clonal architecture and associated somatic mutations. Copy number variation and their boundaries were analyzed by Single Nucleotide Polymorphism (SNP) arrays and mutations by multiamplicon deep sequencing utilizing a panel targeting 60 most commonly mutated genes in myeloid neoplasms. In total cohort 4 % of patients had CUX1 mutations and 6% had locus deletions (affecting ch 7q commonly deleted region: 7q22.1) including 90% of del (7q) cases. Expression of CUX1 is significantly lower in AML with -7/del(7q) compared to AML with normal cytogenetics (p

Description: Myelodysplastic syndromes (MDS) are unique among cancers because of the frequent occurrence of somatic mutations impacting spliceosome machinery. At least 65% of MDS patients harbor a mutation in one of several splicing factors including U2AF1, SF3B1 and SRSF2. Whole exome sequencing of MDS bone marrow uncovered somatic frameshift mutations in LUC7L2, the mammalian ortholog of a yeast splicing factor. LUC7L2 is located in the most commonly deleted region of chromosome 7. Deletions and frameshifts lead to haploinsufficient expression and therefore it can be approximated that a combined 14% of MDS patients have low expression of LUC7L2. Restoring expression of LUC7L2 in del(7q)-iPSCs partially rescues the differentiation of iPSCs into CD45+ myeloid progenitors. Although perhaps partly due to associated losses of other genes on chromosome 7, low expression of LUC7L2 correlates with a poorer patient prognosis, so its haploinsufficiency may play an important role in bone marrow failure. While U2AF1, SF3B1, and SRSF2 are well-characterized splicing factors, the function of LUC7L2 in pre-mRNA splicing is unexamined and its role in the MDS pathogenesis is undefined. We hypothesize that low expression of LUC7L2 results in the aberrant splicing of oncogenes and tumor suppressor gene transcripts thus reducing expression or altering function and contributing to the pathogenesis of MDS. We have characterized LUC7L2 as an alternative splicing regulatory protein that plays a repressive role in the regulation of alternative RNA splicing. We generated HEK-293 cells overexpressing V5-tagged LUC7L2 for immunoprecipitation-mass spectrometry, to ascertain protein interactions with LUC7L2. LUC7L2 co-immunoprecipitated with splicing regulators which are involved in splice site recognition. We performed cross-linking-IP-high-throughput-sequencing (CLIP-seq) to identify LUC7L2 binding sites on RNA. We identified 301 LUC7L2 RNA-binding sites as well as binding sites on U1 and U2 which is common for splicing regulatory proteins. Metagene analysis of these binding sites showed that LUC7L2 bound near splice sites in exonic sequences. We knocked down LUC7L2 expression in HEK293 and K562 cells to phenocopy the frameshifts and deletions observed in MDS patients. We used a PCR-based assay to measure the splicing efficiency of introns near LUC7L2-binding sites. Knockdown of LUC7L2 increased the splicing efficiency of 8/13 selected introns; this suggests that LUC7L2 represses selective splice site usage. We also performed RNA-seq to characterize global mis-splicing events. Analysis of RNA transcripts revealed a multitude of splicing changes, including enhanced exclusion of alternative introns. Knockdown LUC7L2 cells exhibited-altered expression of other splicing factors; this could have further contributed to the vast number of splicing changes observed. To identify specific splicing changes that could contribute to the pathogenesis of MDS, we compared the splicing profiles of LUC7L2-knockdown in K562 cells with RNA-seq data from K562 cells expressing U2AF1S34F, SRSF2P95H or SF3B1K700E. This analysis yielded several exon-skipping splicing patterns in cancer-relevant transcripts, such as oncogene PRC1, splicing factor PTBP1 and MRPL33. Additionally, we noticed commonly mis-spliced transcripts among the four datasets in which the missplicing events occurred in the functional domain, potentially conferring a functional change. Surprisingly, we observed missplicing of U2AF1 in LUC7L2-knockdown, SRSF2P95H, and SF3B1K700E K562 cells, which altered the length of the RNA-recognition UHM domain by inclusion of a mutually exclusive exon or retention of an intron. In this way, low expression of LUC7L2, or point mutants U2AF1S34F, SRSF2P95H, and SF3B1K700E,could alter U2AF1 function as a distal convergence point. In summary, we identified a novel splicing factor implicated in the pathogenesis of MDS. We characterized LUC7L2 as a splicing repressor and discovered many splicing changes caused by low expression of LUC7L2. Several genes were also mis-spliced in U2AF1S34F, SRSF2P95H and SF3B1K700E K562 cells targeting these for further study. Commonly mis-spliced targets such as U2AF1 may indicate that some of the novel therapeutics may have spliceosome mutation agnostic effects. If this applies to the LUC7L2 mutations, then they may also be effective in del7/del7q cases. Disclosures Carraway: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; FibroGen: Consultancy; Jazz: Speakers Bureau; Novartis: Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees; Balaxa: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Agios: Consultancy, Speakers Bureau. Sekeres:Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees. Saunthararajah:Novo Nordisk, A/S: Patents & Royalties; EpiDestiny, LLC: Patents & Royalties. Maciejewski:Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy.

Description: Background. Acquired aplastic anemia (AA) is a bone marrow failure syndrome, in which patients' hematopoietic stem cells are destroyed, resulting in pancytopenia. The exact mechanism and biological process leading to AA remain largely unknown. Bone marrow destruction is perceived as an immune-mediated process, which is supported by elevated cytotoxic T lymphocyte (CTL) counts, responsiveness to immunosuppressive therapy and skewed CTL T cell receptor (TCR) repertoire. Although there is a well-established role of T cells in the pathology of AA, the putative antigen behind the autoimmune response, and thus, the TCRs recognising the antigen are still unrevealed. Methods. Our cohort comprised of 130 samples, consisting of bone marrow and/or peripheral blood samples from AA patients (n=52) from diagnosis and/or follow-up phases and from healthy controls (n=27). We performed TCRβ sequencing (immunoSEQ, Adaptive Biotechnologies) on sorted AA and healthy CTLs (n=25 and n=27) or AA mononuclear cells (MNCs, n=27). To gain in-depth understanding of the TCR-repertoire, we built two novel analysis methods: 1) an unsupervised clustering method to characterize epitope-specific T cells based on the amino acid -level similarities of TCRs and 2) a probability based classifier to identify AA based on TCR data in the CTL (discovery) and MNC (validation) cohorts. Results. To fully appreciate the complex nature of AA pathology, we divided the cytotoxic T cell response in two distinct categories, private and public response. Private response comprises T cell clones found only in individual patients, and this compartment could explain the variation in treatment responses and disease severity across patients. In the CTL TCR repertoire, we identified patient exclusive expanded T cell clones (〉1% frequency in TCR repertoire, n = 317) and treatment responding clones (n = 364). Clustering analysis revealed that there is significant amino acid -level similarity between the TCRs of the private clones. We found several epitope-specific TCR clusters that were associated with different treatment responses, disease severities and HLA-DR15 risk-allele positivity. Interestingly, we discovered that the public response (CTL clones that are statistically enriched in AA patients compared to healthy controls) could discriminate AA from healthy TCR repertoire. Based on these publicly enriched TCRs, we built a classifier which could identify AA CTL TCR repertoire from healthy controls with 97% accuracy (F-score, revieved from leave-one-out cross-validation). We tested our classifier with the validation cohort. The accuracy to diagnose AA based on the TCR repertoire data in the MNC cohort was 0.72. Furthermore, the public clones that differentiated best the AA cases from healthy controls showed statistically significant peptide similarity. These public TCRs occurred also in healthy controls, but with smaller frequencies. When combining the private and public response TCRs, we discovered that some of AA patients' most expanded and treatment responding clones clustered in the same putative epitope-specific clusters with the public TCRs, showing an interesting intersection between public and private signatures. In addition, the comparison of the interesting private and public TCRs against databases of TCR sequences of known antigen specificities hinted that some of these clones may originally target known viral species (CMV, EBV and Influenza A), suggesting a role of these common pathogens in the development of AA. Discussion. CTLTCR repertoire analysis of AA patients revealed a TCR signature that was typical of AA patients, but varied between different patients, and it was validated with an independent dataset. Thus, we could design a TCR-based framework which could identify AA patients based on their TCR repertoire, independently of sample type. A future application for our classifier could be distinguishing AA from other AA-like diseases, like hypoplastic myelodysplastic syndrome. Furthermore, we found groups of TCRs that look similar on amino acid level, and hence these clones may target the same epitope. These TCR clusters were associated with clinical features. Amino acid similarity -based TCR signatures or TCR classifiers have not previously been published for AA or any other autoimmune diseases, and thus our pioneering tools could be utilized to study pathogenesis of other T cell mediated diseases as well. Figure 1. Figure 1. Disclosures Ebeling: Boehringer Ingelheim: Consultancy; Celgene: Speakers Bureau; Otsuka Pharma Scandinavia AB: Consultancy. Maciejewski:Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Apellis Pharmaceuticals: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Ra Pharmaceuticals, Inc: Consultancy. Mustjoki:Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria; Ariad: Research Funding; Pfizer: Honoraria, Research Funding.

Description: Background Patients (pts) with myelodysplastic syndromes (MDS) have heterogeneous outcomes that can range from months for some pts to decades for others. Although several prognostic scoring systems have been developed to risk stratify MDS pts, survival varies even within discrete categories, which may lead to over- or under-treatment. Deficits in discriminatory power likely derive from analytic approaches or lack of incorporation of molecular data. Here, we developed a model that uses a machine learning approach to analyze genomic and clinical data to provide a personalized overall outcome that is patient-specific. Method Clinical and mutational data from MDS pts diagnosed according to 2008 WHO criteria were analyzed. The model was developed in a combined cohort from the Cleveland Clinic and Munich Leukemia Laboratory and validated in a separate cohort from the Moffitt Cancer Center. Next generation targeted deep sequencing of 40 gene mutations commonly found in myeloid malignancies was performed. Pts who underwent hematopoietic cell transplant (HCT) were censored at the time of transplant. A random survival forest (RSF) algorithm was used to build the model, in which clinical and molecular variables are randomly selected for inclusion in determining survival, thereby avoiding the shortcomings of traditional Cox step-wise regression in accounting for variable interactions. Survival prediction is thus specific to each pt's particular clinical and molecular characteristics. The accuracy of the proposed model, compared to other models, was assessed by concordance (c-) index. Results Of 2302 pts, 1471 were included in the training cohort and 831 in the validation cohort. In the training cohort, the median age was 71 years (range, 19-99), 230 pts (16%) progressed to AML, 156 (11%) had secondary/therapy-related MDS, and 130(9%) underwent HCT. Risk stratification by IPSS: 391 (27%) low, 626 (43%) intermediate-1, 280 (19%) intermediate-2, 104 (7%) high, 104 (7%) missing, and by IPSS-R: 749 (51%) very low/ low, 336 (23%) intermediate, 190 (13%) high, 92 (6%) very high, and 104 (7%) missing. Cytogenetic analysis by IPSS-R criteria: 65 (4%) very good, 1060 (72%) good, 193 (13%) intermediate, 60 (4%) poor, and 93 (6%) very poor. The most commonly mutated genes were: SF3B1 (26%), TET2 (25%), ASXL1 (20%), SRSF2 (15%), DNMT3A (12%), STAG2 (8%), RUNX1 (8%), and TP53 (8%). All clinical variables and mutations were included in the RSF algorithm. To identify the most important variables that impacted the outcome and the least number of variables that produced the best prediction, we conducted several feature extraction analyses which identified the following variables that impacted OS (ranked from the most important to the least): cytogenetic risk categories by IPSS-R, platelets, mutation number, hemoglobin, bone marrow blasts %, 2008 WHO diagnosis, WBC, age, ANC, absolute lymphocyte count (ALC), TP53, RUNX1, STAG2, ASXL1, absolute monocyte counts (AMC), SF3B1, SRSF2, RAD21, secondary vs. de novo MDS, NRAS, NPM1, TET2, and EZH2. The clinical and mutational variables can be entered into a web application that can run the trained model and provide OS and AML transformation probabilities at different time points that are specific for a pt, Figure 1. The C-index for the new model was .74 for OS and .81 for AML transformation. The new model outperformed IPSS (c-index .66, .73) and IPSS-R (.67, .73) for OS and AML transformation, respectively. The geno-clinical model outperformed mutations only (c-index .64, .72), mutations + cytogenetics (c-index .68, .74), and mutations + cytogenetics +age (c-index .69, .75) for OS and AML transformation, respectively. Addition of mutational variant allelic frequency did not significantly improve prediction accuracy. When applying the new model to the validation cohort, the c-index for OS and AML transformation were .80, and .78, respectively. Conclusion We built a personalized prediction model based on clinical and genomic data that outperformed IPSS and IPSS-R in predicting OS and AML transformation. The new model gives survival probabilities at different time points that are unique for a given pt. Incorporating clinical and mutational data outperformed a mutations only model even when cytogenetics and age were added. Disclosures Nazha: MEI: Consultancy. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Sallman:Celgene: Research Funding, Speakers Bureau. Roboz:Otsuka: Consultancy; Orsenix: Consultancy; Celgene Corporation: Consultancy; Daiichi Sankyo: Consultancy; Pfizer: Consultancy; Cellectis: Research Funding; Argenx: Consultancy; Roche/Genentech: Consultancy; Celltrion: Consultancy; Sandoz: Consultancy; Aphivena Therapeutics: Consultancy; Bayer: Consultancy; Pfizer: Consultancy; Aphivena Therapeutics: Consultancy; Eisai: Consultancy; Sandoz: Consultancy; Eisai: Consultancy; Roche/Genentech: Consultancy; AbbVie: Consultancy; Novartis: Consultancy; Janssen Pharmaceuticals: Consultancy; Bayer: Consultancy; Celltrion: Consultancy; Novartis: Consultancy; Janssen Pharmaceuticals: Consultancy; Astex Pharmaceuticals: Consultancy; Daiichi Sankyo: Consultancy; Celgene Corporation: Consultancy; Jazz Pharmaceuticals: Consultancy; Jazz Pharmaceuticals: Consultancy; Cellectis: Research Funding; Otsuka: Consultancy; Orsenix: Consultancy; Argenx: Consultancy; Astex Pharmaceuticals: Consultancy; AbbVie: Consultancy. List:Celgene: Research Funding. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Apellis Pharmaceuticals: Consultancy. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Sekeres:Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees.

Description: Myelodysplastic syndromes (MDS) are a complex, heterogeneous group of disorders characterized by the accumulation of somatic mutations in combinations that vary between patients. While individual mutations have been identified that can risk stratify patients or identify targets for therapies, these findings have been relevant to only a minority of patients, as MDS is a disease not of individual mutations but of combinations of genes acting in molecular networks corresponding to several functional and biological pathways. Further, although two patients with MDS may not have mutations in common, they may share a network affected by similar genes. We performed exome sequencing of 201 samples from the bone marrow and peripheral blood of patients with MDS, MDS/MPN, and secondary AML (sAML). Network interactions were retrieved from several publically available databases (InACT, MINT, STRING, etc) and uploaded into cytoscape (an open source software platform for visualizing molecular interaction networks and biological pathways). Functional interactions and pathways were uploaded from Reactome and visualized in cytoscape using Reactome Functional Interaction (FI) network function (Reactome WIKI). Survival analyses were calculated from time of diagnosis to last follow up or death on samples with clinical data. Overall, 3452 mutations were detected with a median of 25 mutations per sample. Network-based analyses identified 745 genes with 293 interactions. Pathway enrichment analysis of the network identified novel pathways that have not been described previously in MDS including: Robo receptor signaling pathway, EphB-Abl signaling pathway, amb2 integrin signaling pathway and NOD-like receptor pathway. Standard clustering analysis (networks with high connections between nodes within the cluster but sparse connections with nodes in different clusters) identified 6 molecular subtypes of MDS, Figure 1. Pathway enrichment analysis of each subtype identified distinct pathways for each: subtype 1 was enriched mainly in immune mediated pathways, RAS/RAF/MAP kinase signaling pathway, EGFR signaling pathway, VEGF signaling pathway, and ERBB signaling pathway; subtype 2 enriched in spliceosome and RNA polymerase transcription pathways; subtype 3 enriched in mitosis and cell cycle pathways; subtype 4 enriched in cadherin and Wnt signaling pathways; subtype 5 enriched in DNA and histone methylation pathways; and subtype 6 with TP53 and DNA damage pathways. To determine the biological importance of the identified subtypes on outcome, we investigated whether each subtype affected clinical characteristics and overall survival. Overall, clinical data was available for 126 patients. Median age was 70 years, 66% have MDS, 17% MDS/MPN, and 17% s AML, 53% have low risk, 21% intermediate, and 26% high risk by the Revised International Prognostic Scoring System (IPSS-R). Clinical characteristics correlated with molecular subtypes: subtype 6 patients were older (median age 76) with higher blasts percentage (median 7%), 50% had sAML, and 20% RAEB-2 (higher risk by IPSS-R), whereas subtype 3 patients were younger (median age 65), has lower blasts percentage (median 2%) and 83% of them had lower risk MDS by IPSS-R. Excluding samples with overlapping subtypes, the median overall survival for patients with subtype 1,2,3,4,5,6 was 33.0, 24.6, 46.6, 22.9, 25.7, 6.6 months, respectively, p= 0.002. Given similar survival for subtypes 2,4, and 5, these were combined in one group, Figure 1. To further identify potential genes in our network for targeted therapies, we searched the publically available targeted therapies databases (TARGET and Therapeutic Target Database). We found 30 potential compounds either in clinical trials or under development that could be explored in MDS. In conclusion: network-based analyses defined molecular subtypes of MDS that were predictive of survival. It also identified potential targets for novel therapies that are in clinical trials or under development. These subtypes may be useful in the development of precision medicine strategies that are specifically directed at the pathways that are enriched in each subtype. Figure 1. Network-based analysis subtypes of MDS and overall survival Figure 1. Network-based analysis subtypes of MDS and overall survival Disclosures Sekeres: TetraLogic: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees.

Description: Despite documented success of immunosuppressive therapy (IST) in the treatment of aplastic anemia (AA), a significant minority of patients remain refractory, most responses are incomplete, and allogeneic stem cell transplantation is not available for older patients or those with significant comorbidities. Until introduction of the cMpl agonist eltrombopag, anabolic steroids were the most commonly used salvage drugs. At least theoretically, engaging growth factor receptors with eltrombopag has the potential to promote the evolution or expansion of mutant clones and thereby increase the rate of progression to secondary MDS, a feared complication of AA occurring in 10-20% of patients. Recently we and others reported detection of clonogenic somatic mutations typical of MDS in patients with AA and PNH. Subsequent study demonstrated that mutations characteristic of sMDS can be found in some patients at presentation of AA and may constitute risk for subsequent progression to MDS. As the risk of MDS evolution was a prominent concern when filgrastim was more widely used in management of AA, now similar questions have been raised regarding use of eltrombopag, be it as salvage therapy or to complement IST. Recently, one of our primary refractory patients receiving eltrombopag progressed to AML. This clinical observation led to investigation of the impact of eltrombopag on evolution and clonal expansion using deep sequencing of a cohort of patients with AA. DNA from bone marrow cells was sequenced before and after initiation of eltrombopag to evaluate clonal expansion or evolution using a targeted multi-amplicon deep sequencing panel of the top 60 most commonly mutated genes in MDS. Among 208 AA patients treated at Cleveland Clinic, we identified 13 patients (median age 68 yrs.) who were treated with eltrombopag for IST-refractory AA; median duration of treatment was 85 wks. The overall response rate, defined as sustained improvement in blood counts and transfusion independence after 12 weeks of therapy, was 46% (6/13), while 38% (5/13) of patients showed stable disease with intermittent transfusions (one of whom underwent HSCT). Among the two non-responders, one patient developed a PNH clone and another progressed to AML (see below). Expansion of PNH granulocytes after eltrombopag treatment was observed in 2 patients. Two patients had chromosomal abnormalities at initial diagnosis, one with t (10; 18) in 2 metaphases, and one with an extranumeral Y chromosome. Use of next generation sequencing (NGS) allows for the quantitative detection of clonal events. We hypothesized that serial analysis by NGS before and after eltrombopag therapy may provide clues as to potential effects of this drug on clonal evolution. Sequencing analysis before eltrombopag treatment revealed the presence of a sole clonal mutational event in 3/13 cases, including CEBPA, EZH2, and BCOR. In the patient with a CEBPA mutation, the mutation persisted during treatment with minimal clonal expansion evidenced by a change in VAF from 53% to 65%. In the second patient, NGS results revealed the initial presence of an EZH2 mutation. A post eltrombopag sample clearly identified acquisition of additional clonal events in genes highly associated with advanced disease and clonal evolution (RUNX1 and U2AF1), as well as slight expansion of a persistent EZH2 clone from 2 to 8%. The third patient harbored a BCOR mutation which expanded markedly, increasing from 8% to 21%, and was accompanied by a hematological response. Sequencing results after eltrombopag treatment revealed the acquisition of new somatic mutations in 5/13 (38%) cases: 2 new CEBPA mutations, 1 new BCOR mutation, and, as discussed, one case with an initial EZH2 mutation in which RUNX1 and U2AF1 mutations were later discovered. In the 5th patient, evolution to AML was observed and accompanied by a large DNMT3A and U2AF1 clone that was absent on initial evaluation. In conclusion, we did observe occasional expansion of clones with potentially leukemogenic mutations during treatment with eltrombopag. At our institution a case control study of patients with refractory aplastic anemia without treatment with eltrombopag is ongoing; ideally a prospective trial would be needed to confirm results. Our results suggest that the initial detection of certain somatic mutations (CBL, SETBP1 and RUNX1) associated with post-AA MDS may contraindicate use of eltrombopag in AA. Disclosures Sekeres: Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; TetraLogic: Membership on an entity's Board of Directors or advisory committees.

Description: Background Several recurrent somatic mutations have been identified in MDS and these mutations play an important role in disease pathophysiology and outcome. BCOR and BCORL1 are located on chromosome X and interact with histone deacetylases and other cell functions. The BCOR gene is mutated (BCORMUT) in 4-6% of MDS patients (pts) and is associated with poor outcome. BCORL1 mutations (BCORL1MUT ) are present in

Description: Recurrent somatic nonsense PHF6 mutations have been reported in patients with T-acute lymphocytic leukemia, AML and chronic myeloid leukemia in blast crisis. Germ line (GL) PHF6 mutations are responsible for Borjeson−Forssman−Lehmann syndrome (BFLS), a hereditary X-linked disorder characterized by mental retardation and dysmorphic features. PHF6 is a highly conserved 41kDa protein with ubiquitous expression in hematopoietic cells, including CD34+ cells. We screened patients (N=1166) with myeloid neoplasms by targeted multi-amplicon deep NGS targeting all ORFs of PHF6 to determine the prevalence and distribution and molecular context of PHF6 gene alterations. In total, we identified and verified 52 cases with somatic PHF6 mutations, 32 of which were frameshift or nonsense mutations and with a strong male predominance (76%). Mutations were distributed almost equally between 2 DNA binding domains. Previously, PHF6 has been included in other screening panels (Haferlach et al. 2014 and Papaemmanuil 2013) with somatic mutations found in 24/944 and 21/738 MDS cases, respectively. SNP-array karyotyping showed that microdeletions involving the PHF6 locus were present in about 1.2% of myeloid neoplasms, but affected only female patients. The most frequent chromosomal aberration observed in conjunction with PHF6 mutations was trisomy-8 (P=.018). The most commonly associated somatic mutations included RUNX1 (P=.001) and IDH1 (P=.008) but not IDH2 (P〉.1). There was no impact on overall survival with respect to PHF6 mutant status in total or within individual risk groups (low risk (RA,RARS) vs. high-risk (RAEB1/2). Concomitant PHF6 and RUNX1 mutations were associated with particularly poor prognosis. RUNX1 mutational status correlated with PHF6 expression levels and PHF6 expression inversely correlated with RUNX1 mRNA levels. Subsequent analysis of clonal architecture using VAF calculations and serial samples for these cases suggested that PHF6 may function as a founder driver gene in 18% of cases. PHF6 variant allelic frequency (VAF) varied between disease subtypes, with the highest clonal burden found in AML patients (P

Description: TET2 is one of the most commonly mutated genes in myeloid neoplasia. The frequency of TET2 mutations (TET2MUT) increases with age and they have been found in aging healthy controls, in whom their presence was associated with a subsequent risk of developing a myeloid neoplasm. The TET2 gene product is an enzyme that uses alpha-ketoglutarate (αKG) and vitamin C to hydroxylate 5-methylcytosine (5MC), leading to both passive demethylation during cell replication and active demethylation via base excision DNA repair enzymes. Despite large studies, there is no consensus opinion as to the clinical impact of TET2 mutations and their mechanistic role in the pathogenesis of MDS. It is likely that the heterogeneity of TET2MUT, their configuration, sub-clonal context and co-associated variables result in biological heterogeneity that precludes proper assessment of clinical impact. To address these issues we analyzed a cohort of 4974 patients with myeloid neoplasms using targeted deep sequencing of a panel of 60 genes found to be most frequently affected by somatic mutations in myeloid neoplasms. A total of 1861 TET2 alterations were identified as somatic in 1238 cases using various bioanalytic algorithms and sequencing of germline DNA where possible. Of these mutations, 80% were frame shift/stop codons (fs/sc) likely leading to various truncations, while 20% were missense (ms) mutations. While no hotspots for mutations were found, 53% were located in the proximity of the catalytic domain. The most recurrent ms alteration was p.Ile1873Thr, found in 2% of all TET2MUT cases. Biallelic TE2MUT were present in 45% of mutant cases; of which fs/sc alteration combinations were the most common (333/557; 60%), and an additional 9% were homozygous fs/sc variants. Biallelic ms mutations were present in 5% of biallelic cases (3% of homozygous ms configurations). Clinically, TET2MUT were found in 17% of MDS patients, 65% of MDS/MPN, 12% of MPN, 21% of non-core binding factor pAML, and 26% of sAML, and were most frequently associated with normal cytogenetics (p

Description: Proteasome inhibitors (PIs) capitalize on the constitutive activation of NF-KB in AML cells and increase chemosensitivity to anthracyclines and cytarabine. We combined the second generation PI, ixazomib, with the standard AML salvage regimen of MEC (mitoxantrone, etoposide, cytarabine). The primary objectives of this study were to determine the dose limiting toxicity (DLT), maximum tolerated dose (MTD), and phase 2 dose of ixazomib in combination with MEC in relapsed/ refractory (R/R) AML. Secondary objectives included evaluating the efficacy of this combination and correlating response to the gene expression profile and CD74 expression, which may identify a subset of leukemias in which NF-KB is operative with increased sensitivity to PI (Attar et al. CCR 2008; 14: 1446-54). Methods: Patients (pts) were treated at Cleveland Clinic and University Hospitals of Cleveland from Oct 2014 to present. An IND was approved by the FDA, and the protocol was approved by each institutional review board. Eligibility: age 18-70 yrs, R/R AML, and cardiac ejection fraction ≥ 45%. The fraction of blasts positive for CD74 was assessed by flow cytometry. Samples were stored for gene expression profiling pre- and post-treatment (at the time of response assessment). Pts received MEC: mitoxantrone (8 mg/ m2), etoposide (80 mg/m2), and cytarabine (1000 mg/m2) intravenous (IV) Days 1-6. Ixazomib, provided by Takeda, was given orally on Days 1, 4, 8, and 11 and was dose escalated using a standard 3x3 design. Dose levels (DLs): 1 (1.0 mg), 2 (2.0 mg), 3 (3.0 mg), 4 (3.7 mg). An additional 18 pts were to be treated at the MTD. One cycle of treatment was administered. Response was assessed by bone marrow aspirate/ biopsy by Day 45 and complete remission (CR) was defined by IWG criteria (Cheson 2006). Toxicities were graded according to NCI CTCAE v 4.03. Toxicities secondary to neutropenia or sepsis were not considered DLTs. DLTs included: (1) ≥ Grade 4 non-hematologic toxicity (NHT) with the exception of nausea, vomiting/ alopecia and drug-related fevers; (2) any ≥ Grade 3 neurologic toxicity; (3) grade 4 platelet or neutrophil count 50 days beyond the start of chemotherapy and not related to leukemia; (4) any Grade 4 NHT 〉 grade 2 by 45 days beyond the start of chemotherapy. Grade 2, 3, and 4 hyperbilirubinemia were redefined as 1.5-〈 10x upper limits of normal (ULN), 10-20 x ULN, and 〉 20 x ULN. Results: Of 23 pts enrolled, 22 are evaluable. The median age was 58 yrs (range 31-70), 12 (52%) were male and the median baseline WBC was 2.56 K/ uL (range 0.1-62.9). The median time from initial diagnosis to registration was 7.1 months (range 1.4-36.8) and 7 pts (30%) had a history of an antecedent hematologic disorder. Thirteen pts were in 1st relapse and 10 pts were refractory to their last therapy. One pt had received a prior allogeneic hematopoietic cell transplant (AHCT), 7 pts had FLT3 ITD mutations and 7/ 21 pts (33%) had adverse cytogenetics per CALGB 8461 criteria at the time of relapse. At DL1, 1 DLT occurred (grade 4 thrombocytopenia), so this DL was expanded to 6 pts. At DL2, 2 pts developed Grade 4 thrombocytopenia; therefore, the MTD of ixazomib was 1.0 mg. The most common grade 3-5 NHTs in the dose escalation phase were febrile neutropenia (100%), hypoalbuminemia (25%), hypokalemia (42%), hypotension (33%), and respiratory failure (33%). No adverse events in the dose escalation phase were attributed to ixazomib alone. The overall response rate was 55% [CR/ CR with incomplete count recovery (CRi)], and 9 pts proceeded to AHCT. Five of these 9 pts remain alive with a median follow-up of 12.8 months. Five pts had CD74 expression performed. Two pts had high levels of CD74 expression (〉 80%); and both achieved CRi. Myeloid mutation panel data was available in 14 pts. Previous data has demonstrated the number of mutations in DNTMT3A, TP53, ASXL1, and NRAS (0, 1, 〉1) is associated with a worse response to salvage therapy (Advani et al, abstract 3825, ASH 2015). Seven pts had at least one of these mutations and 6 of the 7 achieved CR/ CRi. Conclusions: The combination of MEC and ixazomib was well-tolerated and produced an overall response rate of 55% in patients with relapsed/ refractory AML irrespective of molecular mutation status. The combination is safe with a similar toxicity profile to MEC alone. CD74 expression may represent a biomarker for response to this therapy. Results from gene expression profiling will be complete by the time of the meeting and will be presented. Disclosures Mukherjee: Novartis: Consultancy, Honoraria, Research Funding; Ariad: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Caimi:Genentech: Speakers Bureau; Gilead: Consultancy; Roche: Research Funding; Novartis: Consultancy. Maciejewski:Alexion Pharmaceuticals Inc: Consultancy, Honoraria, Speakers Bureau; Apellis Pharmaceuticals Inc: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Speakers Bureau. Sekeres:Millenium/Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.