Abstract: The Muller F element (4.2 Mb, ~80 protein-coding genes) is an unusual autosome of Drosophila melanogaster; it is mostly heterochromatic with a low recombination rate. To investigate how these properties impact the evolution of repeats and genes, we manually improved the sequence and annotated the genes on the D. erecta, D. mojavensis, and D. grimshawi F elements and euchromatic domains from the Muller D element. We find that F elements have greater transposon density (25–50%) than euchromatic reference regions (3–11%). Among the F elements, D. grimshawi has the lowest transposon density (particularly DINE-1: 2% vs. 11–27%). F element genes have larger coding spans, more coding exons, larger introns, and lower codon bias. Comparison of the Effective Number of Codons with the Codon Adaptation Index shows that, in contrast to the other species, codon bias in D. grimshawi F element genes can be attributed primarily to selection instead of mutational biases, suggesting that density and types of transposons affect the degree of local heterochromatin formation. F element genes have lower estimated DNA melting temperatures than D element genes, potentially facilitating transcription through heterochromatin. Most F element genes (~90%) have remained on that element, but the F element has smaller syntenic blocks than genome averages (3.4–3.6 vs. 8.4–8.8 genes per block), indicating greater rates of inversion despite lower rates of recombination. Overall, the F element has maintained characteristics that are distinct from other autosomes in the Drosophila lineage, illuminating the constraints imposed by a heterochromatic milieu.

Abstract: Intermediate-risk acute myeloid leukemia (IR-AML) is a clinically heterogeneous disease, for which optimal post-remission therapy is debated. The utility of next-generation sequencing information in decision making for IR-AML has yet to be elucidated. We retrospectively studied 100 IR-AML patients, defined by European Leukemia Net classification, who had mutational information at diagnosis, received intensive chemotherapy and achieved complete remission (CR) at Cleveland Clinic (CC). The Cancer Genome Atlas (TCGA) data were used for validation. In the CC cohort, median age was 58.5 years, 64% had normal cytogenetics, and 31% required 〉 1 induction cycles to achieve CR1. In univariable analysis, patients carrying mutations in DNMT3A , U2AF1 , and EZH2 had worse overall and relapse-free survival. After adjusting for other variables, the presence of these mutations maintained an independent effect on survival in both CC and TCGA cohorts. Patients who did not have the mutations and underwent hematopoietic cell transplant (HCT) had the best outcomes. HCT improved outcomes for patients who had these mutations. RUNX1 or ASXL1 mutations did not predict survival, and performance of HCT did not confer a significant survival benefit. Our results provide evidence of clinical utility in considering mutation screening to stratify IR-AML patients after CR1 to guide therapeutic decisions.

Abstract: Background Myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML) are mainly diagnosed based on morphological changes in the bone marrow. The diagnosis can be challenging in patients (pts) with pancytopenia with minimal dysplasia, and is subject to inter-observer variability. Somatic mutations can be identified in either disease but no genes, in isolation or in combination, are specific for disease phenotype. We developed a geno-clinical model that uses mutational data, peripheral blood values, and clinical variables to predict an MDS vs. CMML diagnosis in pts who presented with cytopenias, in the absence of bone marrow biopsy results. Method We combined genomic and clinical data from 1897 pts treated at our institution (593) and the Munich Leukemia Laboratory (1304). Pts were diagnosed with MDS or CMML according to 2008 WHO criteria. Diagnosis of MDS and CMML was confirmed by independent hematopathologists that were not associated with the study. A genomic panel of 40 genes commonly mutated in myeloid malignancies was included. The initial cohort was randomly (computer generated) divided into learner (80%) and validation (20%) cohorts. Multiple machine learning algorithms were applied to predict the phenotype. Feature extraction algorithms were used to extract genomic/clinical variables that impacted the algorithm decision and to visualize the impact of each variable on phenotype. Prediction performance was evaluated according to the area under the curve of the receiver operator characteristic (ROC-AUC) and confusion/accuracy matrices. Results Of 1897 pts included, 1368 pts had MDS and 529 had CMML. The median age for the entire cohort was 72 years (range, 11-102); 37% were female. The median white blood cell count (WBC) was 5.1x109/L (range, 0.60-176), absolute monocyte count (AMC) 0.19 x109/L (range, 0-96), absolute lymphocyte count (ALC) 0.77x109/L (range, 0-62), absolute neutrophil count (ANC) 2.44x109/L (range, 0-170), hemoglobin (Hgb) 10.2 (range, 3.9-19.6), and platelet (Plt) count 111x103/mL (range 2-1491). The most commonly mutated genes in all pts were: TET2 (33%), ASXL1 (26%), SF3B1 (21%), SRSF2 (16%), RUNX1 (12%), DNMT3A (10%), CBL (7%), U2AF1 (7%), STAG2 (6%), EZH2 (6%), ZRSR2 (6%), NRAS (6%). In CMML, they were: TET2 (51%), ASXL1 (43%), SRSF2 (25%), RUNX1 (18%), CBL (16%), KRAS (12%), NRAS (11%), EZH2 (9%), JAK2 (6%), U2AF1 (5%), SF3B1 (4%), and DNMT3A (3%). In MDS, they were: TET2 (27%), SF3B1 (24%), ASXL1 (21%), SRSF2 (13%), DNMT3A (12%), RUNX1 (10%), STAG2 (8%), U2AF1 (8%), ZRSR2 (7%), TP53 (7%), BCOR (5%), and EZH2 (5%).The median total number of mutations/sample was 2 (range 0-27) for all pts, 2 (range 0-8) for CMML, and 2 (range 0-27) for MDS. A set of 83 genomic/clinical variables were evaluated and several feature extraction algorithms were used to identify the least number of variables that have the most significant impact on the algorithm's decision. These variables included: AMC, ALC, TET2, ANC, ASXL1, SF3B1, Hgb, number of mutations/sample, AEC, age, Plt, splenomegaly, RUNX1, NRAS, CBL, U2AF1, STAG2, DNMT3A, TP53, EZH2, SRSF2, and ZRSR2, Figure 1. When applying the model to the validation cohort, the ROC-AUC was .98 with an accuracy of 94%, with other statistical values as follows: specificities CMML 93%, MDS 96%; sensitivities CMML 96%, MDS 93%; positive predictive values CMML 84%, MDS 98%; negative predictive values CMML 98%, MDS 84%. Individual pt data can also be entered into the model, with a probability of whether the diagnosis is MDS vs. CMML provided along with the impact of each variable on the decision, as shown in Figure 1. When the analysis was restricted to mutations only, the accuracy of the model dropped dramatically (77%, ROC-AUC .85). Conclusions We propose a novel approach using interpretable, individualized modeling to predict MDS vs. CMML phenotypes based on genomic and clinical data without the need for bone marrow biopsy data. This approach can aid clinicians and hematopathologists when encountering pts with cytopenias and a diagnosis suspicious for MDS vs. CMML. The model also provides feature attributions that allow for quantitative understanding of the complex interplay among genotype, clinical variables, and phenotype. Disclosures Sekeres: Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Savona:Boehringer Ingelheim: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding. Gerds:CTI Biopharma: Consultancy; Celgene: Consultancy; Apexx Oncology: Consultancy; Incyte: Consultancy. Sallman:Celgene: Research Funding, Speakers Bureau. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; 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. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:MEI: Consultancy.

Abstract: 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.

Abstract: Genomic data has led to the identification of bio-markers of morphological features and disease sub-entities in myeloid neoplasia (MN). Somatic TET2 mutations (TET2MT) are frequently found in MN, particularly in chronic myelomonocytic leukemia (CMML). TET2MT are mostly loss-of-function and hypomorphic hits leading to inactivation of TET2 protein. In fact, impaired TET2 activity skews the differentiation of hematopoietic stem cells toward proliferating myeloid precursors favoring myeloid tumorigenesis. However, the contribution of TET2MT to clinico-hematological features in MN has been controversial, possibly due to studies containing too few patients relative to the combinatorial diversity of co-occurring lesions. We recently reported on the clonal architecture of TET2MT in patients with MN. Of these, 40% of the patients harbored biallelic TET2MT (biTET2MT). Further analysis showed a frequent occurrence of biallelic TET2 inactivation (biTET2i). To date, only a few studies have investigated the clinical consequences of biTET2i in MN. We hypothesized that the presence of biTET2i identifies a group of patho-morphological features that independently define a distinct MN subtype. To test our hypothesis, we studied correlations between mutational configuration, clinico-hematological/morphological features and survival outcomes in cases that were biTET2ivs. not (biTET2-), combining whole exome and targeted deep sequencing, SNP-arrays and conventional cytogenetics. Among 1,001 clinically annotated MN patients, 82 were biTET2i (66 biTET2MT, 13 hemizygous TET2MT and 3 homozygous TET2MT, i.e. UPD) and 919 were biTET2- (96 monoallelicTET2MT and 823 wild type). TET2 hits were ancestral lesions in 72% of biTET2ivs. 38% in biTET2- cases (P 〈 .0001). When the 1stTET2 hit was ancestral in biTET2i, the most common subsequent hit was a 2ndTET2MT, followed by SRSF2MT, ASXL1MT, KRASMT/NRASMT and DNMT3AMT. Truncation mutations (frameshift or nonsense variants) were found in 83% of biTET2ivs. 65% of biTET2- cases (P=.02). A second TET2 hit in biTET2MT cases significantly increases the accrual of additional truncating changes. Furthermore, biTET2i were significantly enriched for additional hits in SRSF2MT (33%; P 〈 .0001) and KRASMT/NRASMT (16%; P=.03) while biTET2- for TP53MT (11%; P=.03). SRSF2MT was also found to be significantly associated with biTET2i when compared to monoallelicTET2MT (P=.02). In contrast, biTET2i cases showed absence of SRSF2MT in the absence of monocytosis. We then assessed associations of biTET2i with specific genotype/phenotype. Clinical analyses revealed that cases with biTET2i compared to cases with biTET2- were older (91% ≥60 years vs. 74%, P=.0004) and more commonly had normal karyotype (65% vs. 45%; P=.0007). BiTET2i were enriched in patients with CMML1/2 (44% vs. 9%; P 〈 .0001), and predominantly in lower-risk cases (62% vs. 47% in biTET2-; P=.003). While a second TET2 hit occurred frequently, biTET2i did not portend faster progression but rather associated with monocytic differentiation, consistent with its prevalence in CMML. In addition, among biTET2i with SRSF2MT or KRASMT/NRASMT, CMML was diagnosed in 70% (P=.001) and 77% (P=.01) of the cases, respectively, significantly higher than what was seen in the biTET2i population (44%). In biTET2- cases, leukopenia (81%; P 〈 .0001), neutropenia (52%; P=.008), pancytopenia (27%; P=.008) and increased marrow blast percentages (≥5% in 33%; P=.01) were more prevalent than in biTET2i cases, which in return co-segregated with monocytosis (84%; P 〈 .0001), marrow hypercellularity (cellularity 〉 70% in 67%; P 〈 .0001) and marked myeloid dysplasia (68%; P=.0003). Given our observation of a highly significant (P 〈 .0001) relationship between biTET2i, CMML diagnosis and/or monocytosis, we also evaluated patients without frank diagnosis of CMML (CMML-) and compared biTET2ivs.biTET2- for associations with monocytosis and myeloid dysplasia, two hallmarks of CMML. Increased monocyte counts among CMML-cases were significantly overrepresented in biTET2i cases (72%; P=.03) vs.biTET2- (55%) as was myeloid dysplasia (72% vs. 46%; P=.0001). Lastly, biTET2i as a sole hit or in combination with other hits did not influence survival outcomes. In sum, biTET2i invariantly associates with distinct morphological and clinical phenotype. It may thus represent an early diagnostic marker of morphologic MN sub-entities. Disclosures Nazha: MEI: Consultancy. Sekeres: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; Opsona: Membership on an entity's Board of Directors or advisory committees. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski: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; Apellis Pharmaceuticals: Consultancy.

Abstract: Treatment-associated myeloid neoplasias (tMN) are serious iatrogenic complications of cytotoxic therapies applied to primary malignancies (PM). With increased therapeutic success rates and prolonged survival of cancer patients, tMN may become more prevalent. Although tMN diagnosis is trivial to the extent that previous therapies are known, tMN may represent coincidental primary MDS/AML not causally linked to chemotherapy (ctx) or radiation (rtx). Other seemingly tMN cases may carry germ line predisposition responsible for co-occurrence of 2 neoplastic processes, and finally others are truly treatment-related MN. With the recognition of clonal hematopoiesis of indeterminate potential (CHIP), it is also likely the preexisting CHIP would be either selected for, eliminated by cancer therapies, or that ctx or rtx lead to emergence of CHIP. tMN may also manifest without antecedent CHIP, and thus could be either CHIP-derived or de novo. Some of the problems in assigning somatic mutational pattern to tMN may be alleviated by application of proper control groups which include sMN (MN after PM treated only surgically). In our cohort of 1058 patients, we identified 109 cases of such sMN, 266 tMN with a history of rtx or ctx for PM, and 683 of primary MN (pMN), having no PM. Of these 65 sMN, 145 tMN, and 683 pMN were sequenced by NGS. Using these three patient groups, we sought somatic mutations that distinguish them. tMN presented as more aggressive disease: diagnosed older vs. pMN/sMN (68 years, p 〈 .001) , shorter latency from PM to MN vs. sMN (8.7 vs. 10.5 years, p=.085), complex cytogenetics vs. pMN, sMN (p=1.4x10-5, p=2.7x10-4) including chromosome 7 (p=1.7x10-7, p=8.4x10-5) and 5 abnormalities (p=.044, p=.09), 50% tMN were advanced MDS/AML vs. 35% pMN, 42% sMN (p=.00016, p=.25). The most common mutations in all 3 groups were TET2, DNMT3A, ASXL1 and SRSF2. Mutations in SF3B1 and JAK2 were less common in tMN vs. pMN, sMN (p=.058, p=.014; p=.011, p=.327) while those in TP53, KIT, EZH2,WT1 were most frequently mutated in tMN vs. pMN/sMN (OR 2.6, p=.002, OR 6, p=.011, OR 1.9, p=.083, OR 3.2, p=.08). Mutations in ETV6 and EZH2 were only found in rtx vs. ctx-treated tMN cases (p=.046, p=.004). TP53 mutations were associated with ctx (OR 7.2, p=0.062), and when combined with cases which received both ctx and rtx vs. rtx alone, TP53 mutation was 9.3x was common (p=.014). In tMN TP53 and EZH2-mutated cases, a higher proportion of transversions was observed vs. those found in pMN (p=.055, p= 0.052). The domains mutated in TP53 tMN vs. pMN/sMN cases were similar, while EZH2-mutated tMN were enriched for hits in domain 2 (p=0.0604). This suggests that the type of treatment utilized influences the molecular signature of tMN in terms of frequency of mutations as well as types of mutations found. A meta-analysis of 9 CHIP studies was performed to pool overall frequencies of MN-related genes. These frequencies were compared to those of ancestral events (determined by recapitulation of clonal hierarchy via variant allele frequency and zygosity) in our cohort. This yielded 3 categories of mutations: those that are CHIP-derived (frequency in CHIP 〉 ancestral frequency in MN), from de novo MN (mutations in gene not seen in CHIP), or those found in both CHIP and MN, termed mix-derived. CHIP-derived hits were TET2, DNMT3A, JAK2 while STAG2, EZH2, APC, MLL, WT1 were de novo hits. Using this categorization scheme tMN break down as 19% CHIP-derived, 38% de novo, and 24% mix-derived. CHIP-derived tMN were, on average, 6 years older than de novo tMN (p=.019). This also held true for the age of PM diagnosis, where CHIP-derived cases were, on average, 10 years older than de novo tMN (p=.0175), suggesting that age of PM may be correlated with acquisition of CHIP in tMN. CHIP-derived vs. de novo tMN latencies did not differ. The molecular signatures of tMN are influenced by therapies utilized for PM as well as CHIP. Targeted sequencing for germ line predisposition genes is under way for these patients to further characterize their molecular profiles. Disclosures Nazha: MEI: Consultancy. Gerds:Apexx Oncology: Consultancy; Incyte: Consultancy; Celgene: Consultancy; CTI Biopharma: Consultancy. Carraway:Jazz: Speakers Bureau; Balaxa: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; FibroGen: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees; Novartis: Speakers Bureau; Agios: Consultancy, Speakers Bureau. Sekeres: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; Opsona: Membership on an entity's Board of Directors or advisory committees. Maciejewski:Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; 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; Ra Pharmaceuticals, Inc: Consultancy.

Abstract: Background Acute myeloid leukemia (AML) is a complex, heterogeneous neoplasm characterized by the accumulation of complex genetic alterations that are responsible for the initiation and progression of the disease. Secondary AML (sAML) represents a progression from antecedent hematologic disorders such as myelodysplastic syndromes (MDS) or myeloprolifrative neoplasms (MPN). Certain acquired mutations have been reported to be specific for sAML when compared to primary AML (pAML), but many limitations exist when cytogenetic grouping or other parameters are taken into account. In addition, some mutations have been shown to impact survival in some studies, but not others. Methods We performed targeted deep sequencing on samples from bone marrow and peripheral blood of pts diagnosed with sAML and pAML and treated at our institution between 1/2003-1/2013. Additional data on pAML was added from The Cancer Genome Atlas (TCGA). A panel of 62 gene mutations described as frequently recurrent mutations in myeloid malignancies were assessed. Cytogenetic grouping was defined by CALGB/Alliance criteria. Differences were compared using Fisher's exact test and the Mann-Whitney U test for categorical and continuous variables, respectively. Overall survival (OS) was calculated from the time of diagnosis to last follow up or death. Results: A total of 496 pts included: 273 with pAML and 223 with sAML. Comparing pAML to sAML, pts were younger (median age 59 vs. 68 years, p 〈 .001) and had a higher WBC at diagnosis (13.5 vs. 3.9 X 109/L, p 〈 .001), respectively. Cytogenetic analysis showed significant differences: 58% of pAML pts had normal karyotype (NK) compared to 37% of sAML (p=.002), whereas 24% and 26% of sAML had intermediate risk (other than NK) and complex karyotype ( 〉 3 abnormalities) compared to 11% and 16% for pAML (p 〈 .001, .009), respectively. Mutations in ASXL1 (p 〈 .001), JAK2 (p=.014), CBL (p=.05), BCOR (p=.02), STAG2 (p =.003), SF3B1 (p=.04), SRSF2 (p=.001 ), and U2AF1 (p=.03) were highly specific for the sAML phenotype, whereas mutations in NPM1 (p 〈 .001 ), FLT3 (p 〈 .001), DNMT3A (p 〈 .001), and IDH2 (p=.02) were more specific for pAML. When the analysis was restricted to pts with NK cytogenetics, only ASXL1 (p 〈 .001) remained specific for sAML and DNMT3A (p 〈 .001) for pAML.Further, when the analysis was restricted to pts with unfavorable risk cytogenetics, only ASXL1 (p=.01) remained specific for sAML. No other mutations were specific for pAML. We then evaluated whether the mutations that were specific to each AML phenotype had an impact on OS. We observed different mutations that impacted OS in each phenotype: DNMT3A (HR 1.81, 95% CI 1.28-2.57, p 〈 .001), TP53 (HR 3.1, 95% 1.74-5.53, p 〈 .001), and SUZ12 (HR 3.18, 95% CI 1.01-10, p=.05) led to worse OS in pAML, whereas mutations in EZH2 (HR 2.12, 95% CI 1.07-4.21, p =.03), PRPF8 (HR 2.32, 95% CI 1.20-4.46, p=.01), and TP53 ( HR 2.92, 95% CI 1.69-5.04, p 〈 .001) lead to worse OS in sAML. Different mutations had a different impact on OS when cytogenetic analysis was taken into account. Mutations in FLT3 (HR 2.15, 95% CI 1.37- 3.35, p 〈 .001) and DNMT3A (HR 2.41, 95% CI 1.57-3.70, p 〈 .001) led to worse OS in NK pAML, whereas none of the mutations impacted OS in NK sAML. Further, in pAML with unfavorable cytogenetics, BCOR (HR 2.41, 95% CI 1.57-3.70, p 〈 .001) and TP53 (HR 2.41, 95% CI 1.57-3.70, p 〈 .001) had led to worse OS, whereas BOCR (HR 2.95, 95% CI 1.03-8.50, p 〈 .001), SF3B1 (HR .19, 95% CI .05-.82, p 〈 .001), SUZ12 (HR .12, 95% CI .01-.99, p 〈 .001),and TP53 (HR 1.9, 95% CI 1.09-3.46, p 〈 .001) only impacted OS in sAML. Conclusion Clear genomic variations exist between sAML and pAML. Although some of these genomic changes are more specific to each phenotype in general, this specificity and the impact on OS differed for each cytogenetic subgroup, highlighting the complexity of interpreting genomic information in pts with AML and the need to incorporate both cytogenetic and molecular data in prognosis-driven treatment decisions. 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.

Abstract: 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 〈 1% of MDS pts with an unknown impact on OS. We investigated the clonal architecture of BCOR and BCORL1 in MDS and the impact of these mutations on clinical outcome. Methods We sequenced DNA samples from pts with MDS and related myeloid malignancies (MDS/myeloproliferative neoplasms (MPN) and secondary AML) for the presence of BCOR and BCORL1 mutations and 58 other genes that have been described as recurrently mutated in myeloid malignancies. The Revised International Prognostic Scoring System (IPSS-R) was calculated as descried previously. Overall survival (OS) was measured from the time of diagnosis to time of death or last follow up. Variant allele frequencies (VAFs) adjusted by zygosity were used to define architecture of driver clones. Results Of 621 included MDS pts, 29 (5%) had BCOR mutations and 13 (2%) had BCORL1 mutations. Patients with BCOR mutations were younger (median age 63 vs. 68 years, p= .04), and had a lower platelet counts at diagnosis (63 vs. 93 109/L, p = .01) compared to BCORWT pts. Cytogenetic risk categories per IPSS-R for BCORMUT was similar to BCORWT: very good 3% vs. 2%, good 65% vs. 62%, intermediate 10% vs. 17%, poor 13% vs. 7% and very poor 6% vs. 9%, respectively, p = .62). Risk categories per IPSS-R were also similar for BCORMUTcompared toBCORWT:very low 10 vs. 16%, low 31 vs. 40%, intermediate 24 vs. 18%, high 17 vs. 14%, and very high 13 vs. 10%, respectively, p = .69). BCOR mutations were missense in 9 pts (31%), frameshift insertion/deletion in 10 (34%), stopgain in 8 (28%), and nonsense in 2 (6%). BCOR is commonly co-mutated with ASXL1 (p=.008), RUNX1 (p= .0001), NF1 (p =.002), ETV6 (p =.026), BCORL1 (p =.0001), MECOM (p =.021), RAD21 (p =.021), and CEBPA (p = .0001). Clonal architecture analysis identified BCOR mutations as ancestral, subclonal, and mosaic in 41%, 21% and 38% of cases respectively. The median OS for BCORMUT pts was 24.5 months compared to 17.9 months for BCORWT, p = .23). The impact of BCOR mutations on OS was neutral even after adjustment for age and IPSS-R risk categories. Pts with BCORL1MUT had lower WBC counts (median 2.7 vs. 4 109/L p = .02), and lower platelets counts at diagnosis (72 vs. 91 109/L, p = .02) compared to BCORL1WT pts. In cytogenetic analyses, BCORL1MUT was associated with a higher incidence of very good cytogenetics per IPSS-R (15% vs. 2%, p =.001). Further, BCORL1MUT were more likely to be classified in the intermediate risk group per IPSS-R (46 vs. 18%, p = .01) and less likely to be classified in the low risk group (7 vs. 40%, p = .01) compared to BCORL1WT. BCORL1 mutations were missense in 7 patients (53%), frameshift insertion/deletion in 3 (23%), stopgain in 1 (7%), and nonsense in 2 (15%). BCORL1 is commonly co-mutated with TET2 (p =.001), BCOR (p 〈 .001) DHX29 (p = .001), C7orf55 (p =.02), and FLT3 (p =.03). Clonal architecture analysis identified BCORL1 mutations as ancestral, subclonal, and mosaic in 61%, 23% and 15% of patients respectively. The median OS for BCORL1MUTpts was longer compared to BCORL1WT, but was not significantly different (43.8 vs. 19.9, respectively, p = .16) even after adjustment for age and IPSS-R risk categories. Conclusion BCOR and BCORL1 mutations occur with low frequency in MDS. These mutations can be ancestral or subclonal. The impact of BCOR mutations on OS was neutral even after adjustment for age and IPSS-R risk scores. Although the median OS for pts with BCORL1MUT was longer compared to BCORL1WT,it was not statistically significant. A larger pt population with BCOL1 may show a positive impact of these mutations on OS. Disclosures Mukherjee: Celgene: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Ariad: Consultancy, Honoraria, Research Funding. Advani:Blinatumomab: Research Funding; Pfizer Inc.: Consultancy, Research Funding. 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.