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Background: Down syndrome (DS) acute myeloid leukemia (DS-AML) is predominantly found in young children (under the age of 4) and responds well to chemotherapy. The difference in clinical features suggests that despite the predominance of megakaryoblastic disease, DS-AML blasts are different from non-DS AML blasts with megakaryoblastic morphology (AMKL). The immunophenotypes of DS-AML blasts at diagnosis were compared to those of non-DS AML patients using unsupervised hierarchical clustering analysis (HCA) and assessed for their immunophenotypic overlap with known morphologic subgroups. In addition, residual disease monitoring of non-DS AML patients using "Difference from Normal" flow cytometry has been applied to DS-AML with unexpected results. Methods: A standardized antibody panel was used to define quantitative gene product (antigen) expression in bone marrow (BM) specimens from patients with AML at diagnosis and following chemotherapy. The quantitative phenotypic expression of multiple cell surface markers and physical characteristics defined a position in 15 dimensional data space for each patient. HCA was performed to mathematically cluster DS-AML patients enrolled on Children's Oncology Group study AAML1531 with non-DS patients from study AAML0531 based on 15 dimensional profiles. Five patients with DS who were older than 4 years of age and therefore treated on AAML0531 were also included. Data regarding morphology were appended to the data set. The same antibody panel was used to assess measurable residual disease (MRD) in 147 follow up BM aspirates from 50 DS patients ages 1 to 21 undergoing treatment for either AML or B-ALL (n=46, n=4 respectively) who were not included in the clinical studies. When possible, cell sorting was performed for FISH or SNP/CGH microarray studies. Results: Diagnostic Clustering: HCA of 17 DS-AML diagnostic phenotypes with 769 non-DS AML patients revealed a tight immunophenotypic correlation between 12 (71%) DS-AML patients (Figure 1). Further analysis of this diagnostic subset showed a consistent mean antigen expression profile that was distinct from non-DS AML diagnostic phenotypes. Similarly, the DS-AML patients clustered separately from the 5 older DS patients (>4 years of age) with AML, who were treated on AAML0531 and exhibited consistently different identifying diagnostic markers despite the shared constitutional trisomy 21. Additionally, the phenotype associated with the DS-AML cluster does not match that of patients with AMKL morphology. The largest immunophenotypic cluster of non-DS patients with AMKL morphology were classified with the RAM phenotype[1], which segregate into a mathematically unique branch of the dendogram distinct from the DS-cluster. Observation of a consistent DS-specific non-leukemic myeloid regenerative phenotype: Of the 147 post chemotherapy specimens submitted for MRD analysis off clinical study, 141 (96%) had a clearly identifiable CD34+/CD56+ population present between 0.06-6.0% total non-erythroid composition. A second abnormal population expressing CD34+ but not HLA-DR was also observed in these patients but at a lower level (0.02-2.66%) compared to the CD34+/CD56+ cell population. Two patients had a reproducible absence of 56+, DR- progenitors across all follow up specimens (6/147) and may represent unique clinical cases with disease features beyond our knowledge. In four DS patients undergoing treatment for B-ALL, the same immunophenotypic features (CD34+/CD56+ and negative HLA-DR) were observed. Genetic data: In 4 cases, cell sorting of CD34+/CD56+ post treatment progenitors revealed a lack of cytogenetic markers present in the clone of leukemic blasts at diagnosis. Conclusions: The blasts of patient with typical DS-AML (i.e under the age of 4 years at diagnosis) are immunophenotypically distinct from those of non-DS-AML patients and exhibit consistent immunophenotypic features that differ from DS patients with AML who are older than 4 years of age. The normal myeloid progenitors in DS patients who have undergone chemotherapy for AML or ALL express an immunophenotype that is different from normal BM of non-DS patients recovering from chemotherapy. [1] Brodersen, Lisa Eidenschink, et al. "A Recurrent Immunophenotype at Diagnosis Independently Identifies High Risk Pediatric AML: A Report from the Children's Oncology Group Trial AAML0531." Disclosures Wilson: Hematologics: Employment. Brodersen:Hematologics: Employment. Zehentner:Hematologics: Employment, Equity Ownership. Pardo:Hematologics, Inc: Employment. Wells:Hematologics: Employment, Equity Ownership. Loken:Hematologics: Employment, Equity Ownership.

Background:Flow cytometric studies are useful in the diagnostic workup of patients with unexplained cytopenias and it has been demonstrated that bone marrow aspirates with immunophenotypic abnormalities by flow cytometry but not diagnostic morphologic or cytogenetic findings frequently evolve into myelodysplastic syndromes (MDS) (Kern 2013). Two flow cytometric scoring systems (FCSSs), the Wells FCSS and the Ogata FCSS, have diagnostic and prognostic utility. The Wells FCSS utilizes a difference from normal algorithm incorporating more than ten phenotypic parameters. The accumulation of these abnormalities is not only useful in diagnosis but is predictive of patient outcome (Wells 2003, Scott 2008, Alhan 2014). The recommended Ogata FCSS has evolved to include four cardinal parameters: (1) CD45 intensity on the myeloid progenitors, (2) frequency of lymphoblasts, (3) frequency of myeloid progenitors, and (4) granularity of the maturing myeloid cells. The Wells FCSS is more comprehensive as it uses more phenotypic characteristics, while the Ogata score is considered straightforward to implement in a routine setting (Della Porta 2012, Ogata 2009). This study compares the Wells FCSS and Ogata FCSS for sensitivity and specificity to detect clonal abnormalities documented by SNP/CGH microarray and conventional cytogenetics. Patients and Methods: The cohort included 99 patients with unexplained cytopenias whose bone marrow aspirates were submitted for SNP/CGH microarray and flow cytometry (HematoLogics). The immunophenotypic data were independently assigned a Wells FCSS (Cutler 2012) and an Ogata FCSS (Della Porta 2012). SNP/CGH microarray was assessed for MDS-associated genetic abnormalities. The findings were further correlated with conventional cytogenetic findings. Results: Of the 99 bone marrow aspirates, 20 exhibited clonal abnormalities associated with MDS. The Wells FCSS identified immunophenotypic abnormalities suggestive of MDS for 18 of 20 CGH positive specimens (sensitivity of 90%) and did not detect phenotypic abnormalities suggestive of MDS in 68 of 79 CGH negative specimens (specificity of 86%). In contrast the Ogata FCSS identified immunophenotypic abnormalities suggestive of MDS for 13 of 20 CGH positive specimens (sensitivity of 65%) and did not detect phenotypic abnormalities suggestive of MDS in 64 of 79 the CGH negative specimens (specificity of 81%). In an attempt to improve the sensitivity and specificity of the Ogata score, the granularity parameter was modified from side scatter channel mode of the granulocytes (compared to the side scatter mode of the lymphocytes) to the side scatter channel at the 15thpercentile of granulocytes (compared to the mean of lymphocytes). This modified parameter detected all specimens defined as hypogranular by the side scatter mode, and detected an additional 11 specimens as hypogranular. All of these specimens were detected as hypogranular by the Wells definition. This modified granularity method was then used along with the other three cardinal parameters to create a modified Ogata FCSS. The granularity modification resulted in improved sensitivity (70% versus 65%); specificity was unchanged. While the modified method outperformed the original, it did not match the performance of the Wells FCSS. Conclusions: In patients with unexplained cytopenias, the Wells FCSS demonstrates superior specificity and sensitivity than the Ogata FCSS for detecting myeloid immunophenotypic clones associated with SNP/CGH array and cytogenetic abnormalities. Modifying the Ogata granularity parameter marginally improves the sensitivity but does not improve the specificity. Implementation of the Wells FCSS requires a comprehensive understanding of phenotypic intensities and relationships in non-clonal hematopoiesis for patients with cytopenias. While the relative ease of implementing the Ogata FCSS is attractive, improvements are essential for diagnostic accuracy; improving the granularity parameter alone is not sufficient. Adding measurements for the maturing myeloid and erythroid compartments may increase the diagnostic utility of the Ogata FCSS but requires further study. Disclosures Brodersen: Hematologics Inc.: Employment. Menssen:Hematologics Inc.: Employment. Zehentner:HematoLogics Inc.: Employment, Equity Ownership. Stephenson:Hematologics Inc.: Employment. de Baca:Hematologics Inc.: Employment. Johnson:Hematologics Inc.: Employment. Singleton:Hematologics Inc.: Employment. Hartmann:Hematologics Inc.: Employment. Loken:Hematologics: Employment, Equity Ownership. Wells:HematoLogics Inc.: Employment, Equity Ownership.

Background: Myelodysplastic syndromes (MDS) are associated with cytogenetic clones. To follow the maturation sequence of original clones and evolved subclones with additional cytogenetic abnormalities, progenitor cells, immature and mature myeloid cells were sorted by flow cytometry and analyzed separately by fluorescence in situ hybridization (FISH) and comparative genomic hybridization (CGH). Methods: Flow cytometry sorted cell fractions from bone marrows for sixteen patients with MDS-associated cytogenetic abnormalities were evaluated by FISH. Flow cytometric cell sorting was based on CD34+/low side scatter (SS) for progenitors, CD13+/CD16-/high SS for immature myeloid cells, and CD13+/CD16+/high SS for mature myeloid cells. Customized color labeling (spectrum orange, green and aqua) of FISH probe combinations were designed to detect and to analyze clonal evolution for each patient based on their known cytogenetic abnormalities and clonal evolution patterns. Three marrow aspirates were sorted for analysis by SNP/CGH microarray. Results: The 16 bone marrow specimens evaluated by FISH were categorized into three groups: (1) eight patients with a single, good-to-intermediate cytogenetic abnormality; (2) four patients with monosomy 7; and (3) four patients with more than one chromosome abnormality with evidence of clonal evolution by conventional cytogenetic analysis, excluding monosomy 7. The Group-1 abnormalities included deletion 20q (n=4), trisomy 8 (n=2), deletion 5q (n=1), and trisomy 11 (n=1). All specimens from this group showed FISH abnormalities in equal proportions in myeloid progenitors, immature and mature myeloid cells. Group-2 had four patients with monosomy 7. All four had monosomy 7 concentrated in the progenitor cells (45-79%) compared to immature and mature myeloid compartments (less than 9-36%). For Group-3, known original clones with single cytogenetic abnormalities (deletion 20q, monosomy 3, deletion 7q or 5q) were monitored by FISH analysis. Using customized FISH panels, the presence of subclones with additional cytogenetic aberrations (trisomy 8 in three patients and gain of a marker chromosome characterized by the centromere of chromosome 4 in a fourth patient) was assessed using single-cell resolution. The progenitor and immature myeloid compartments had the original founding clones containing a single cytogenetic abnormality at 15-34% and the subclones with the additional aberrations at 23-76%. In contrast, the mature myeloid cells were comprised of the original clone at 20-40%, but the subclones with additional aberrations were absent in the mature myeloid compartment in three patients and reduced by more than half in a fourth. For three patients sorted bone-marrow fractions were analyzed by SNP/CGH microarray. In one patient with trisomy 8 as the sole abnormality, the same aberration was observed in both the immature and mature myeloid compartments. In two other patients, additional abnormalities not seen in the mature myeloid cells were detected in the progenitor and immature myeloid compartments. Conclusions: These data show two main patterns for the distribution of clonal cytogenetic abnormalities among myeloid cells in MDS: 1) A continuous distribution at all stages of maturation was found for single aberrations with good-to-intermediate prognostic associations (Group 1) and 2) A disrupted distribution pattern with accumulation of the cytogenetic aberrations in the progenitor compartment as compared to the immature and mature myeloid compartments for specimens characterized by monosomy 7 (Group 2). Similarly, subclones characterized by additional cytogenetic abnormalities (Group 3) were sequestered in the progenitor and immature myeloid compartments while the original founding clone was evenly distributed throughout maturation. These data demonstrate that specific cytogenetic abnormalities associated with poor prognosis (e.g. monosomy 7) as well as acquired cytogenetic abnormalities as a result of clonal evolution can cause disruption of myeloid cell maturation in MDS. Disclosures Zehentner: HematoLogics Inc.: Employment, Equity Ownership. Hartmann:Hematologics Inc.: Employment. Johnson:Hematologics Inc.: Employment. Bennington:HematoLogics Inc.: Employment. Fritschle:HematoLogics Inc.: Employment. Ghirardelli:HematoLogics Inc.: Employment. Broderson:HematoLogics Inc.: Employment. Chapman:Hematologics Inc.: Employment. Stephenson:Hematologics Inc.: Employment. de Baca:Hematologics Inc.: Employment. Singleton:Hematologics Inc.: Employment. Wells:HematoLogics Inc.: Employment, Equity Ownership. Loken:Hematologics: Employment, Equity Ownership.

Background: Waldenström's macroglobulinemia (WM) and lymphoplasmacytic lymphoma (LPL) are lymphoproliferative disorders with bone marrow infiltration by clonal lymphoplasmacytic cells (Treon et al., 2003, 2005). The somatic point mutation L265P in the myeloid differentiation primary response gene 88 (MYD88) has been reported in more than 90% of WM patients (Treon et al., 2012). Therefore MYD88 mutation analysis has been implemented in clinical practice to support the diagnosis of LPL/WM. After the implementation of MYD88 L265P assays with increased detection sensitivity, a substantial portion of patients with IgM monoclonal gammopathy of undetermined significance (IgM-MGUS) was also reported MYD88 L265P positive. Splenic marginal zone lymphoma (SMZL), B-cell chronic lymphoproliferative disorders (B-CLPD) and diffused large B-cell lymphoma (DLBCL) have been found positive with much lower incidence rates (Varettoni et al., 2013; Xu et al., 2013). Hence the remaining need to differentiate WM/LPL from other lymphoproliferative disorders with co-occurring plasma cells with high confidence. Patients and Methods: In this study, flow cytometric cell sorting was utilized to isolate clonal plasma and B lymphoid cell fractions as previously described (Zehentner et al., 2011). 69 patient specimen fractions with a clinical history of WM /LPL, multiple myeloma, CLL and lymphoma were analyzed for MYD88 L265P mutation using Sanger sequencing. Furthermore, the Biomed-2 primer sets for the immunoglobulin heavy (IgH) and/or the immunoglobulin kappa light chain region (IgK) were used to compare B cell clonality profiles in the lymphoid versus plasma cell compartments. Results: MYD88 L265P mutation was detected in all specimens with confirmed Waldenström's macroglobulinemia (17/17, 100%). Of these 16/17 (94%) revealed MYD88 L265P as well as identical monoclonality profile by gene rearrangement analysis in both the plasma and the B lymphoid cell collections. In 47% (8/17), the mutation was only detected in the plasma and B cell fractions, but not in the whole bone marrow specimens. 21 patient specimens with a known clinical history of LPL and co-occurring clonal plasma cells were tested. 9 of 21 (43%) were categorized with identical B cell clonality profile when comparing plasma and B lymphoid cells; whereas 12 (57%) had unrelated clonality profiles. 7 of the 9 (78%) specimens in the identical clonality group tested positive for MYD88 L265P in both the plasma and B lymphoid clone. None of the unrelated clonality group specimens carried the mutation in both cell fractions; for 7/12 (58%) MYD88 L265P was found in either the plasma (2) or the B-cell fraction (5) whereas 5/12 (42%) tested negative. 11 bone marrow aspirate specimen with known presence of lymphoma (including splenic marginal zone (SMZL), mantle cell and marginal zone) were analyzed. 10/11 (90%) tested negative for MYD88 L265P, with the exception of one SMZL specimen. Furthermore, 12 known myeloma, 5 CLL and 3 healthy donor specimens were analyzed, all tested negative. Conclusions: In this study, we developed and tested a novel approach to assess MYD88 L265P mutation status in order to assist WM/LPL diagnosis. Flow cytometric cell sorting for clonal plasma and B cell populations was combined with molecular analysis. Subsequently, MYD88 L265P mutation as well as B-cell clonality profile was compared in both cell fractions. Our study postulates a significant improvement in sensitivity and most importantly specificity when applying MYD88 L265P mutation status in combination with cell sorting for WM/LPL diagnostic decisions. 47% WM patients (8/17) and 44% LPL patients (4/9) were positive for the MYD88 mutation exclusively in both flow cytometry sorted cell fractions but not in whole bone marrow specimens. 94% (16/17) WM as well as 78% (7/9) LPL specimens with identical plasma and B-cell clonality profile revealed the presence of the MYD88 L265P mutation in both the plasma cell and the B lymphoid cell clones. Whereas LPL specimens with unrelated clonality profile of the plasma and lymphoid cell fractions as well as other control specimens (lymphoma, myeloma, CLL and healthy) either tested negative or positive only in one of the sorted cell fractions. We therefore conclude that confirming the presence of MYD88 L265P in both B-lymphoid and plasma cell fraction is an important prerequisite to distinguish LPL/WM from related disorders with high confidence. Disclosures Wang: HematoLogics Inc.: Employment. Fritschle:HematoLogics Inc.: Employment. Bennington:HematoLogics Inc.: Employment. Burnworth:HematoLogics Inc.: Employment. Bennington:HematoLogics Inc.: Employment. Wentzel:HematoLogics Inc.: Employment. Verkamp:HematoLogics Inc.: Employment. Nguyen:HematoLogics Inc.: Employment. Ghirardelli:HematoLogics Inc.: Employment. Broderson:HematoLogics Inc.: Employment. Wells:HematoLogics Inc.: Employment, Equity Ownership. Loken:HematoLogics Inc.: Employment, Equity Ownership. Zehentner:HematoLogics Inc.: Employment, Equity Ownership.

Introduction: Myeloid malignant disorders are clonal diseases arising in hematopoietic stem or progenitor cells. Several somatic mutations involved in these diseases are currently known and routine molecular testing involves screening genes of therapeutic and prognostic significance. Mutational analysis of FLT3 in combination with NPM1 can be used to predict outcome and direct therapy in normal karyotype acute myeloid leukemia (AML). JAK2, MPL and CALR mutation detection complements the molecular diagnostic testing menu for myeloproliferative neoplasm’s (MPN): polycytemia vera (PV), essential thrombocythemia (ET) and primary myelofibrosis (PMF). In addition, these molecular markers are utilized for minimal residual disease (MRD) detection, e.g. following stem cell transplants. Little is currently known about the lineage-specific distribution of some of these markers. In this study we aimed to assess the distribution of common genetic mutations in multiple lineages (lymphoid, myeloid, monocyte, multipotent progenitors, myeloblast and erythroid) of MPN and AML utilizing fluorescent activated cell sorting (FACS). Method: Different cell lineage fractions (lymphoid (CD3+), mature (CD16+) and immature (CD16dim) granulocytes, monocyte (CD14+), erythroid(CD36+), multipotent progenitors (34+) and/ or myeloblasts (CD117+) of unseparated bone marrow and peripheral blood specimens of myeloid disorders were sorted on a BD Aria 2. The patient specimens selected were positive for either JAK2V617F, MPLW515L or CALR Exon9 insertion/ deletion (MPN’s) or for NPM1 and/or FLT3 mutations (AML; diagnostic, relapse and minimal residual disease (MRD)). Fractions were subsequently analyzed for the presence of the respective mutation by PCR and/ or bi- directional sequencing. Results: All FACS purified CD34+ progenitors, myeloid and erythroid cell fractions of MPL W515L (3) or CALR exon9 (12) positive MPN specimens demonstrated the presence of mutations, respectively. Interestingly, JAK2V617F was present in the sorted erythroid cell fraction in 5/6 MPN cases tested. However, the granulocyte cell and blast cell fraction of one polycythemia vera specimen tested negative for the presence of Jak2V617F. All lymphoid CD3+ T-cell fractions were negative. The NPM1 exon 12 mutation was uniformly detected in progenitors and all myeloid cell fractions of 3/3 diagnostic, 2/2 relapse and 1/8 MRD AML specimens. For 5/8 MRD cases all lineages tested negative. Surprisingly, for two MRD cases the mutation was observed only in the unseparated and myeloid lineages but not in CD34+ blast fraction. Similar to the above findings, FLT3 mutations were detected in multipotent progenitors, and/ or myeloblasts collections of 4/4 diagnostic specimens. However, the mutation was absent in the granulocyte and monocyte fraction of one case. No detectable signals were observed in the cell fractions of 5 MRD specimens and in the CD3+ lymphoid cell fractions of all AML cases. Conclusion: We conclude that CALR and MPL mutations are uniformly detectable in the unseparated bone marrow specimens of MPN’s as well as separated progenitor, erythroid, granulocyte and monocyte fractions. Interestingly, JAK2 mutations can be exclusively found in the erythroid lineage in PV, whereas it can be absent in the granulocyte and blast compartment. This finding may have implications on specimen processing to ensure that erythroids are retained for clinical Jak2 testing. In addition, our results support the hypothesis that CALR Exon9 mutations are early event driver mutations in comparison to JAK2V167F. Both NPM1 and FLT3 mutations, in AML, were detected in unseparated specimens as well as in the multipotent progenitors or myeloblasts at diagnosis and relapse. However, the NPM1 mutation was observed in unseparated specimens and granulocyte cell fractions of 2 residual disease cases, whereas it was surprisingly absent in the CD34+ cell lineage fractions. Conversely, FLT3-ITD was exclusively found in the progenitor cells and absent in the granulocyte lineage of one case at diagnosis. Our findings reported here may be able to assist assay development efforts for diagnostic and residual disease mutation detection in myeloid disorders. In addition, flow cytometric assessment of monitoring specimens prior to molecular analysis may be beneficial to decide if cell enrichment steps can give additional evidence for the presence of residual disease. Disclosures Burnworth: HematoLogics Inc.: Employment. Bennington:HematoLogics Inc.: Employment. Fritschle:HematoLogics Inc.: Employment. Nguyen:HematoLogics Inc.: Employment. Verkamp:HematoLogics Inc.: Employment. Angela:Hematologics: Employment. Wentzel:HematoLogics Inc.: Employment. Broderson:HematoLogics Inc.: Employment. Loken:Hematologics: Employment, Equity Ownership. Wells:HematoLogics Inc.: Employment, Equity Ownership. Zehentner:HematoLogics Inc.: Employment, Equity Ownership.

Background: Single nucleotide polymorphism (SNP) and comparative genomic hybridization (CGH) microarray analysis is a powerful tool to assess myelodysplastic bone marrow specimens for the presence of genomic gains and losses as well as loss of heterozygosity (LOH) (reviewed by Nybakken & Bagg, JMD 2014). Its application can be a valuable addition to conventional cytogenetic analysis and may be superior to FISH testing for MDS assessment. Currently, microarray analysis does not have widespread use in an MDS work-up. Several groups have demonstrated that flow cytometric analysis can detect phenotypic aberrations in bone marrow aspirates with cytopenias with more abnormalities identified in patients with poor prognosis or with multiple genotypic abnormalities (Loken et al. 2008; Cutler et al. 2011; van de Loosdrecht et al. 2013). In this study SNP microarray results were compared with conventional cytogenetic and MDS panel FISH findings as well as phenotypic abnormalities detected by flow cytometry. Patients and Methods: 185 bone marrow aspirate specimens submitted to our laboratory for MDS work-up were analyzed by SNP/CGH studies. 36 of these (19.5%) were positive by SNP/CGH microarray analysis. 32 of the positive microarray cases (88.9%) were also analyzed by conventional cytogenetic studies, 35 (97.2%) by MDS FISH panel (5p, 7q, +8, -17p, -20q) and 31 (86.1%) were assessed by multidimensional flow cytometry (FCM) and were assigned an FCSS score (Wells et al. 2003). Results: Of the specimens in which the SNP/CGH array demonstrated genotypic abnormalities, 11/32 (34.4%) were negative by conventional cytogenetic analysis while 12/35 (34.3%) showed no abnormalities by MDS FISH panel analysis. SNP/CGH analysis revealed additional chromosomal gains and losses in 18/32 (56%) in comparison to cytogenetic analysis and in 22/35 (63%) in comparison to FISH analysis. Loss of Heterozygosity regions were detected in 28/36 cases (78%) with 96.4% (27/28) of these being larger than 2 Mb and 53% (19/28) spanning a significant chromosomal region (e.g. 1p, 5q, 7q and 17p) with known oncogenic and other MDS related genes. In 10/32 cases (31%), microarray analysis was able to characterize the origin of marker chromosome material, previously reported with unknown identity by conventional cytogenetic analysis. In an additional subset of 10 out of 32 cases (31%), cytogenetic analysis was able to either characterize balanced translocations or low level sub-clonal abnormalities not identified by microarray analysis alone. In 11/36 (31%) microarray analysis was able to detect clonal heterogeneity and evolution. In none of the specimens did FISH analysis detected abnormalities not revealed by microarray analysis. Flow cytometry performed on 31 of the array positive specimens revealed 6 to have >20% abnormal myeloid progenitor cells (classified as AML) while 23 the remaining 25 cases showed phenotypic abnormalities consistent with MDS (FCSS ranging from 1-6). In two specimens with a FCSS of 0, LOH regions on 16q or 1p and 21q were found, respectively, without the presence of numerical aberrations. A FCSS score of 1 with minimal phenotypic abnormalities (n=3), was comprised of one specimen with del(5q), one with LOH of 7q and one with trisomy 8, 1p loss and 1q gain. Specimens with an FCSS of 2 (n=7) showed only one specimen classified as complex (5 or more abnormalities). The two FCSS =3 specimens showed del(5q) with del(12p) and several LOH regions, not complex findings. One of the 4 specimen with FCSS = 4 was classified as complex while the other 3 specimens showed monosomy 7, LOH of 7q or LOH of 1p, respectively. Genotypic abnormalities were also related to phenotypic abnormalities in 4/7 (57%) specimens in the FCSS = 5/6 category which revealed complex microarray findings. Half (3/6) of the AML class had complex findings as well. Conclusions: These results emphasize the additional value that CGH/SNP microarray analysis adds to conventional cytogenetic analysis. Our dataset confirms that FISH studies do not provide additional information for MDS specimens positive by cytogenetic and/or microarray analysis. Most importantly, a high correlation between our phenotypic flow cytometric scoring system for myeloid abnormalities and microarray findings has been identified. Higher flow cytometric abnormality scores correlate with increasing complexity of genomic abnormalities. Disclosures Zehentner: HematoLogics Inc.: Employment, Equity Ownership. Brodersen:Hematologics Inc.: Employment. Stephenson:Hematologics Inc.: Employment. de Baca:Hematologics Inc.: Employment. Menssen:Hematologics Inc.: Employment. Hammock:Hematologics Inc.: Employment. Johnson:Hematologics Inc.: Employment. Hartmann:Hematologics Inc.: Employment. Loken:Hematologics Inc.: Employment, Equity Ownership. Wells:HematoLogics Inc.: Employment, Equity Ownership.