Journal articles on the topic 'Roboshop'

Abstract Laboratory process automation is an important requirement for streamlining and standardizing technical procedures. Despite the extensive use of magnetic cell separation, only the latter steps in these procedures have been automated. Currently magnetic cell labeling is done manually followed by automated magnetic separation (e.g. AutoMACS and Isolex). Additionally, current technology only allows for processing of a single sample at a time. Our objective was to develop a fully automated system to magnetically separate multiple blood and bone marrow samples. The major barrier to automation of cell labeling is that these procedures typically require a centrifugal wash step, which is relatively expensive to automate and requires bulky equipment. We had previously developed a magnetic cell labeling/separation system call EasySep® (Stemcell Technologies) which does not require a centrifugal wash step. We have now fully automated EasySep® and present the RoboSep™ instrument which magnetically labels and separates 4 samples at once, with up to 2×109 total cells per sample or 8×109 total cells. The instrument operates in a standard biosafety hood and uses sterile disposable pipette tips to ensure aseptic operation and avoid cross-contamination between samples. Standardized automation protocols have been developed for both positive and negative selection. With positive selection, the desired cells are magnetically labeled and then purified by a sequence of magnetic wash steps. With negative selection, unwanted cells are magnetically labeled and then depleted. To demonstrate the suitability of RoboSep™ for automated positive selection of hematopoietic progenitors and stem cells, we performed CD34+ cell selection from previously frozen cord blood (CB) and mobilized peripheral blood (MPB). For the CB separations, the CD34+ cell content was enriched from 1.2±0.4% to 96.6±3.1% with a recovery of 45±9% (n=9, mean ± 1 SD). For the MPB separations the CD34+ cell content was enriched from 0.7±0.1% to 96.7±3.1%, with a recovery of 45±13% (n=4). To test RoboSep in negative selection we used an EasySep® antibody cocktail depleting cells that express any of CD2, CD3, CD11b, CD11c, CD14, CD16, CD19, CD24, CD56, CD66b, and glycophorin A to isolate hematopoietic progenitors from bone marrow (BM) and MPB. CB separations required the addition of anti-CD41 to the antibody cocktail for depletion of platelets. The table below shows results for negative selection from BM, CB and MPB. Manual separations performed in parallel with the above automated separations showed comparable purity and recovery, indicating that we have succeeded in automating both positive and negative selection procedures. The RoboSep instrument processes up to 4 tissue samples at once and provides the opportunity to isolate multiple cell subsets from the same sample by combining positive and negative selection methods in a single automated procedure. Negative Selection Results (Mean± 1 SD) Sample % CD34+ in start % CD34+ in enriched % Recovery CD34+ cells Fold-enrichment of total BFU-E, CFU-GM, CFU-GEMM % recovery of total BFU-E, CFU-GM, CFU-GEMM N.A. Not Available CB (n=2) 1.5 67.4 50 36 38 MPB (n=2) 1.1 50.0 45 50 41 BM (n=4) 4.7±3.1 47.5±7.5 N.A. 47±10 71±13

Photometric variability in cataclysmic variables (CVs) on time scales longer than a few days can be most effectively addressed by automated long-term monitoring programs such as that of RoboScope (Honeycutt & Turner 1992): more than 100 CVs have been monitored for about 13 years, obtaining 75 to 150 measurements per year for each system. Among the techniques being explored for analysing this data set is the use of the structure function (SF), an autocorrelation tool employed extensively for the study of the light curves of AGNs (e.g. Hufnagel & Bregman 1992). A first order SF measures the scatter in a time series of magnitudes, m, as as a function of the time lag, τ (Hughes, Aller & Aller 1992).

Abstract Immune ablation followed by allogeneic hematopoietic cell transplantation in humans necessitates hematopoietic cell reconstitution and immune re-education. All blood cell lineages are affected and post-transplantation immune restoration depends upon the graft’s ability to generate both lymphoid and myeloid lineage cells. Decisions regarding immunomodulation treatment post-transplantation are often made on the basis of chimerism testing. Chimerism analysis is typically performed on small blood samples, especially with pediatric patients. Since lymphoid and myeloid engraftment is asynchronous the determination of lineage-specific chimerism is needed. Analysis of purified cell subsets requires techniques which can isolate >1 cell type from a single small starting sample. This avoids dividing the sample. Performing flow cytometry as well as isolation of DNA from the purified subsets means that high cell recovery is essential. Preparation of the sample using a ficoll-based method often results in cell loss of 50% while certain lysis and wash steps can affect granulocyte content. We describe a method of sequential selections to isolate B cells, then T cells and finally myeloid lineages (CD33+ and/or CD66b+) using a fully automated pipetting robot called RoboSep®. RoboSep® can process sample sizes that range from 0.5 to 4.25 ml of human whole blood. CD19 (B cell) positive and CD3 (T cell) positive and myeloid cell fractions are isolated using immunomagnetic, column-free positive selection (EasySep®). Briefly, cells are first labeled with antibody targeting CD19 positive cells. These are then coupled to magnetic nanoparticles and the sample is placed in a magnet. The supernatant with unlabeled cells is removed to a new tube, leaving isolated CD19 positive cells in the magnet. The supernatant is then labeled with anti-CD3 antibody, magnetic nanoparticles, placed in a magnet and the supernatant is removed to a new tube leaving isolated CD3 positive cells. Finally, a cocktail of antibodies (anti-CD33, anti-CD66b) is used to label and select the myeloid cells from the supernatant. The resultant positive cells are collected in the magnet. Assessment by flow cytometry yields average purities over 90% for all cell types. Cell isolation using this method produces highly purified cells in quantities sufficient to generate genomic DNA for chimerism testing, even from very small amounts of starting sample. For example, 2.0 ml of whole blood yields on average 1.3ug of B cell genomic DNA, 10.2ug of T cell genomic DNA and 6.1ug of myeloid cell genomic DNA. In conclusion, we have developed a rapid, fully automated RoboSep® method to sequentially isolate highly purified B cells, then T cells and finally myeloid cells from a single starting sample of whole blood. The number of cells (x106) and amount of total genomic DNA (range) obtained from 2.0 ml of whole blood starting sample (n=3). No. Enriched Cells Total DNA (ug) CD19+ 0.12 – 0.34 1.1 – 1.6 CD3+ 1.8 – 3.2 7.9 – 11.9 Myeloid 2.2 – 2.9 4 – 7.2

With the rapid development of technology and the increasing use of social networks, many opportunities for the design and deployment of interconnected systems arise that could enable a paradigm shift in the ways we interact with cultural heritage. The project described in this paper aims to create a new type of conceptually led environment, a kind of Cyber–Physical–Social Eco-Society (CPSeS) system that would seamlessly blend the real with virtual worlds interactively using Virtual Reality, Robots, and Social Networking technologies, engendered by humans’ interactions and intentions. The project seeks to develop new methods of engaging the current generation of museum visitors, who are influenced by their exposure to modern technology such as social media, smart phones, Internet of Things, smart devices, and visual games, by providing a unique experience of exploring and interacting with real and virtual worlds simultaneously. The research envisions a system that connects visitors to events and/or objects separated either in time or in space, or both, providing social meeting points between them. To demonstrate the attributes of the proposed system, a Virtual Museum scenario has been chosen. The following pages will describe the RoboSHU: Virtual Museum prototype, its capabilities and features, and present a generic development framework that will also be applicable to other contexts and sociospatial domains.

Abstract The immune-editing effect of myeloid leukemia has recently been reported in several studies. We previously demonstrated that the K562 leukemia-derived cell line suppresses T cell proliferation, which suggests that myeloid leukemia may function in a similar way to myeloid derived suppressor cells (MDSC). While the mechanism of suppression in leukemia is not fully understood, recent murine and human studies suggest that the STAT3 and arginase pathways play a key role in the immunosuppressive function of MDSC. We hypothesized that myeloid leukemia utilizes the MDSC STAT3 and arginase pathway to evade immune control, and block anti-leukemic immune responses. To evaluate the suppressive capacity of myeloid leukemia on T cell proliferation, we isolated CD34+ blasts and myeloid derived suppressor cells (MDSC: CD11b+CD14+) from blood of primary leukemia samples by FACS sorting (n=5). These cells were co-cultured with CFSE-labeled CD4+ T cells (n=9), previously isolated from healthy donor PBMCs using an automated cell separator (RoboSep). After stimulating with CD3/CD28 Dynabeads (Invitrogen, New York, USA) for 72 hours, proliferation was measured by CFSE dilution of the viable cell population. In three myeloid leukemias studied, CD4+ T cell proliferation was significantly suppressed in the presence of primary CD34 blasts and MDSC cells (p<0.001). Interestingly, CD34 blasts demonstrated a greater suppressive effect on T cells compared to MDSC cells for these samples (not statistically significant p=0.61). Next we repeated the proliferation assay using five leukemia cell lines: THP-1 and AML1 (derived from AML), K562 and CML1 (derived from CML), and the Daudi lymphoid-derived leukemia cell line. After staining with cell tracer dye and irradiating 100Gy, the cells were co-incubated with CFSE-labeled CD4+ T cells from healthy volunteers (n=6). We found that CD4+ T cell proliferation in the presence of the myeloid leukemia cell lines was significantly suppressed (mean proliferation 5.7±0.9% to 26.1±10.7%: p<0.0001 to 0.05) compared to lymphoid cell lines (mean proliferation 76.3±8.2%: p>0.05), consistent with the results obtained with the primary leukemia samples. To evaluate the impact of STAT3 and arginase on the immunosuppressive function of myeloid leukemia, the five cell lines were primed overnight with either arginase inhibitor (N(ω)-Hydroxy-nor-L-arginine; EMD Biosciences, Inc., California, USA) or two STAT3 inhibitors (STAT3 Inhibitor VI or Cucurbitacin I; EMD Millipore, Massachusetts, USA). Then, CD4+ T cells from healthy donors (n=3) were cultured with either (1) leukemia without any inhibitor (2) leukemia in the presence of inhibitor (3) leukemia primed with inhibitor. Priming leukemia with arginase inhibitor and STAT3 inhibitors almost completely abrogated their suppressive effect of T cell proliferation (p<0.001). We conclude that myeloid leukemia, like MDSC, directly immunosuppresses T cells, through STAT-3 and arginase. This finding may underlie the immune-editing of T cells by myeloid leukemia. Our results suggest that STAT3 inhibitors could be used to augment leukemia-targeted immunotherapy. Further investigation of T cell biology within the leukemia microenvironment is needed to further define immune editing mechanisms in myeloid leukemia. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures: No relevant conflicts of interest to declare.

Abstract Between presentation and remission of AML, loss of leukemia burden and the recovery of normal hematopoiesis are likely to be associated with major changes in cytokine profiles which could inform pathophysiology of hematopoiesis and immune recovery and may be predictive for outcome. However cytokine fluctuations in AML before and after induction chemotherapy are not well characterized. To profile the cytokine signatures of patients with AML, we analyzed 57 cytokines, chemokines, and growth factors in blood of 11 patients with AML (mean age 58 years; 31-69) undergoing conventional remission induction chemotherapy enrolled into an investigational study (VICCHEM 1073). Plasma was obtained from heparinized peripheral blood collected at onset of leukemia, 8-14 days, and 22-35 days after the initiation of induction chemotherapy and also from 12 healthy donors. Cytokine levels were measured in duplicate using magnetic beads based Luminex assay (Affymetrix, CA, USA). Compared with normal controls, 5 cytokine patterns were observed. i) levels significantly lower at onset of leukemia, and lowest 8-14 days after induction correlating with lymphocyte count: GM-CSF, M-CSF, PDGF-AA, EGF, FGF basic, IL1b, IL-2,IL4, IL10, IL12p40, IL12p70, IL13, IL15, IL17a, IL22, IL23p19, TNF beta, TNF alpha, IFN alpha, IFN gamma, TGF alpha, MCP3, LIF, Granzyme B, sFAS ligand, TRAIL, (p<0.05). Most of these cytokines are predominantly produced by T cells or other immune cells. ii) levels significantly higher in AML through chemotherapy induction and recovery: IL-27 (p=0.002), MPO, IL2Ra, IL-21, , IP-10, MIG, MIP1 alpha, SDF-1, MCP1(p<0.05), HGF (p=0.05), VEGF (p=0.07), IL1Ra (p=0.058). These cytokines are predominantly produced by stromal cells. iii) levels significantly higher at the onset of leukemia and correlating with lymphocyte count: CD40 ligand (p<0.05). iv) levels significantly lower at onset of leukemia but inversely correlated with lymphocyte count; Flt3-ligand, sFAS (p<0.05). v) No significant differences and fluctuation: NGF, GRO alpha, IL1a, IL3, IL5, IL6, IL7, IL8, IL9, MIP1b, SCF. Among the cytokines persistently elevated in AML, IL-27 was significantly higher in patients who did not achieve complete remission after induction chemotherapy (p=0.03). To investigate the biological consequences of elevated IL-27 in the AML microenvironment, we examined the effect of IL-27 on T-cell function. Previous studies in mice show that IL-27 rapidly induces PD-L1 expression on naïve CD4+ T-cells. Human CD4+ naïve and CD4+ cells were isolated from healthy volunteers (n=4) according to RoboSep protocols (Stemcell Technologies, Vancouver, Canada), then incubated with IL-27 or IL-6 for up to 72 hours. IL-27 was found to induce PD-L1 expression in a time and dose-dependent manner especially in CD4+ naïve and central memory populations. These findings support other findings that AML suppresses protective antileukemic immune responses and cause T cell exhaustion. IL-27 production induced by AML cells may explain exhaustion of CD4+ T-cells through increased PD-L1 expression. Targeting IL-27 may improve immune function in AML and lead to better survival. Disclosures: No relevant conflicts of interest to declare.

Introduction: Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic neoplasm characterized by sustained peripheral blood (PB) monocytosis and an inherent risk for leukemic transformation. Clonal origins of the disease can be detected in hematopoietic progenitor cells (CD34+/CD38-), while the complete spectrum of mutational evolution can be seen in circulating monocytes (CD14+). Cell sorting strategies have been employed to select cells in CMML, and while there are adequate monocyte numbers in the PB, there are very few circulating progenitor cells. In addition, attrition related to the selection process significantly depletes primary cells available for biological experiments and multiomics studies such as RNA-seq, ChIP-seq, ATAC-seq, and DIP-seq. While single-cell methods may be able to overcome this challenge, bulk sequencing methods remain a robust and cost-effective approach. We hypothesized that, secondary to the stem cell origin of this disease and significant myeloproliferation, PB mononuclear cells (MNC) would provide comparable results with regards to transcriptomic analysis, in comparison to cell selection procedures. Methods: Peripheral blood obtained from 15 molecularly annotated patients with WHO-defined CMML was ACK-lysed and subjected to a Ficoll procedure for collection of MNC. MNC were left unsorted (n=5) or further selected for CD34+/CD38- (n=5) and CD14+ (n=5) using a fully automated RoboSep-S (StemCell Technologies) protocol. All samples were then subjected to bulk whole transcriptome shotgun sequencing (using Illumina TruSeq and an Illumina HiSeq 4000). After data quality control, counts of detectable transcripts were log2-normalized and Pearson's product-moment correlation coefficients were calculated to evaluate the correlation between the two cell-sorting strategies and unsorted cells in terms of detectable transcripts. To visualize sample differences log2-normalized transcripts counts were centered and scaled per gene for a select number of genes relevant to myeloid biology as well as a number of housekeeping genes. Results: Fifteen patients with WHO-defined CMML, median age 69 years (55-73 years), 66% male, were included. Next generation sequencing for somatic mutations was performed on PB MNC obtained at CMML diagnosis (Figure 1, top heatmap). Considering the small sample size, mutations were evenly distributed among groups with the exception of ASXL1 (higher frequency in CD14+ and CD34+/CD38- cells), ZRSR2 (higher frequency in unsorted cells), and TET2 (lower frequency in CD14+ cells). The three groups were also well matched with regards to other CMML-related variables such as WHO and FAB morphological subtypes, cytogenetic abnormalities, and risk stratification by the Mayo Molecular Model. Transcriptomic analysis revealed a strong positive correlation between the median number of log2-normalized detectable transcripts in unsorted cells and CD34+/CD38- cells (ρ = 0.96, p < 0.001, top scatterplot). Likewise, there was a strong positive correlation between the median number of log2-normalized detectable transcripts in unsorted cells and CD14+ cells (ρ = 0.91, p < 0.001, bottom scatterplot). The latter correlation was marginally lower, which was explained by increased global gene expression in 3 of the 5 CD14+ samples (bottom heatmap). Increased gene expression in these 3 samples involved key myeloid genes and housekeeping genes known to have stable expression across human tissues alike. In comparison to PB MNC, both cell sorting strategies resulted in significant depletion of primary cells required for other experiments, and for procedures such as ChIP-seq, DIP-seq and ATAC-seq (CD34+/CD38- had greater depletion than CD14+). Additional experiments to assess this strategy for the above mentioned epigenetic studies are currently being planned. Conclusions: Accounting for sample differences, different cell sorting strategies (unsorted, CD34+/CD38- selection, and CD14+ selection) yielded similar results when performing bulk transcriptomic assessments on PB MNC from patients with CMML. For the purpose of gene expression profiling there was no clear advantage with CD34+/CD38- or CD14+ selection. These results support the use of unsorted cells for bulk transcriptomic analysis in CMML. Figure 1 Disclosures Patnaik: Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.

Abstract Abstract 2812 Poster Board II-788 From cytomorphological aspects, monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM) are overlapping disorders. According to the WHO classification, a threshold of 10% of plasma cells (PCs) in bone marrow (BM) aspirates separates both categories. To clarify whether this separation is justified from cytogenetic aspects, we performed comparison of interphase fluorescence in situ hybridization (FISH) patterns in 691 patients with MGUS (n=381) or MM (n=310). Also, the results of cytomorphology and immunophenotyping using multiparameter flow cytometry (MFC) were correlated. 278 females and 413 males (22.8-92.8 yrs) at first presentation of MM/MGUS were analyzed between 2005-2009 in our laboratory with a combination of cytomorphology, MFC, and FISH following magnetic activated cell sorting (MACS) of CD138+ PCs (Robosep, STEMCELL Technologies, Vancouver). According to cytomorphological WHO criteria, 381 patients were categorized as MGUS (median BM PCs, 5%), 310 as MM (median PCs, 18.5%). The number of FISH probes being applicable to samples was depending on the amount of plasma cells yielded from MACS procedure. In the MM pts, a median of 12 probes was applied (range, 0-22) which was slightly higher than in the MGUS patients (median: 11; 0-18) (p=0.00002). FISH procedure was hampered by insufficient PC numbers more frequently in MGUS (79/381, 20.7%) than in MM pts (29/310; 9.3%) (p=0.0004). In MM pts, FISH revealed a median of 2 and a maximum of 7 cytogenetic alterations per patient in contrast to a median of 0 and a maximum of 5 in MGUS (p<0.0001). In more detail, in MM the maximum number of gains of genetic material per patient was 7 (MGUS: 5), of losses 7 (MGUS: 3), and of reciprocal rearrangements 2 (MGUS: 1). Subsequently, cytogenetic alterations were compared in those 527 pts (260 MM and 267 MGUS pts; 76.3% of all pts), in whom PC numbers allowed performance of at least 5 FISH probes (t(11;14), t(4;14), t(14;16), 13q14, TP53). Abnormal FISH results were detected in 145/260 MM (55.8%) and 106/267 MGUS pts (39.7%) (p=0.0002). In MM, 31/260 (11.8%) had a t(4;14)/IGH-FGFR3 in contrast to 5/268 (1.9%) in MGUS (p<0.0001). The t(11;14)/IGH-CCND1 (MM: n=41; 15.6%; MGUS: n=50; 18.7%; n.s.) and t(14;16)/IGH-MAF were similarly frequent in both cohorts (MM: n=8; 3.1%; MGUS: n=3; 1.1%; n.s.). Monosomy13/del(13)(q14) was more frequent in MM (n=103; 39.3%) when compared to MGUS (n=59; 22.1%, p=0.0001). Deletions of TP53/17p13 were seen in 16 MM (6.1%) and in 6 MGUS pts (2.2%) (p=0.029). Notably, in 7 MGUS cases with <1% PCs in cytomorphology, FISH revealed genetic alterations in 3 cases. PCs as quantified by cytomorphology vs. MFC ranged from 0-96% vs. 0-84% (median 8.5 vs. 2.0), respectively, with a highly significant correlation between both methods (Pearson, r=0.712, p<0.0001). However, as previously reported, MFC detected lower numbers in general: the median ratio of PCs by cytomorphology:MFC amounted to 4.25 (range 0.00-178.00). In 12 MGUS cases (1.7%) as defined with cytomorphology, MFC did not detect any PCs. Conversely, in 5 MGUS cases, MFC detected PCs while cytomorphology did not. In conclusion, cytogenetic patterns showed higher genetic complexity in MM cases when compared to MGUS, and both the t(4;14) as -13/del(13)(q14) were significantly more frequent in MM when compared to MGUS. However, the cytogenetic alterations showed no specific pattern for MM or MGUS categories. Therefore, the overlaps being seen from morphological aspects do also exist on the genetic level. This suggests that a cytogenetically based categorization of these cases might correlate better with the clinical profiles than the cytomorphological separation of MM/MGUS. Finally, this study supports the performance of FISH in MGUS cases. The demonstrated detection of malignant/monoclonal PCs by MFC also in cases in which cytomorphology fails to diagnose MM/MGUS emphasizes the inclusion of MFC in a standard diagnostic procedure. Disclosures: Haferlach: MLL Munich Leukemia Laboratory: Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Equity Ownership. Weiss:MLL Munich Leukemia Laboratory: Employment. Schnittger:MLL Munich Leukemia Laboratory: Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Equity Ownership.

Abstract Abstract 2876 Multiple myeloma (MM) is a malignancy of abnormal plasma cells and a correlation with poor outcome has been described for immunoglobulin heavy-chain (IgH) translocations, deletions of 13q or 17p. Thus far, a convincing relationship between specific mutations, disease onset and progression has not been fully established. Aberrant impairment of important signaling pathways can drive oncogenesis and contribute to MM development. We therefore chose to study NRAS, KRAS and BRAF, three members of the RAS-RAF signaling pathway, as well as TP53 and CCND1, two fundamental genes in cell cycle control. We here investigated 41 MM cases to further elucidate molecular mechanisms underlying this disease. Bone marrow (n=35) or, in case of plasma cell leukemia, peripheral blood (n=6) specimens were collected between 12/2006 and 6/2011 and molecular analyses using a deep-sequencing assay (454, Branford, CT) in combination with the 48.48 Access Array technology (Fluidigm, South San Francisco, CA) were performed on mononuclear cells after Ficoll enrichment or magnetic activated plasma cell sorting using anti-CD138 beads (RoboSep, STEMCELL Technologies SARL, France). The cohort included 16 female and 25 male patients at first diagnosis, with a median age of 63 years (range: 33–84 years). Based on fluorescence in situ hybridization (FISH), the cohort was characterized as follows: IgH rearrangements were detected in 54.3% of patients (19/35: n=6 with t(4;14), n=9 with t(11;14), n=3 with t(14;16), n=1 other; data not available: n=5). A deletion 13q14 was present in 64.9% of patients (24/37; data not available: n=4). Trisomy 3 was detected in 48.0% of patients (12/25; data not available: n=16), trisomy 9 was detected in 50.0% of patients (12/24; data not available: n=17), trisomy 11 was detected in 46.4% of patients (13/28; data not available: n=13), and trisomy 15 was detected in 56.2% of patients (9/16; data not available: n=25), respectively. Interestingly, in all cases where FISH data was available (n=36), at least 1 aberration was detectable. Further, we studied the occurrence of somatic mutations in NRAS, KRAS, BRAF, TP53 and CCND1. In our cohort, we detected an overall mutation rate within the RAS pathway of 41.4% (17/41), in line with a recent report (Chapman et al., Nature, 2011). KRAS was the most frequently mutated gene with 21.9% of cases with mutations (9/41 patients), followed by NRAS (19.5%; 8/41 patients). Recently, BRAF V600E mutations have gained clinical interest since they became manageable by targeted treatment in melanoma. Interestingly, Chapman et al. discovered a mutational rate of 4% by sequencing of 161 MM patients (Nature, 2011). Even if BRAF is not a frequently mutated gene in MM, it justifies upfront diagnostic screening since these patients may benefit from new treatments. In our cohort, 2/41 patients harbored BRAF V600E mutations. Moreover, because of their involvement in the same signaling pathway, we also noticed that mutations affecting NRAS, KRAS or BRAF were predominantly mutually exclusive, except for one patient who concomitantly harbored a BRAF and a NRAS mutation. Additionally, we observed an overall molecular TP53 mutation rate of 12.2% (5/41 patients). In these 5 patients, in total 7 mutations (5 missense substitutions; 2 frame-shift mutations) were detected. 1/4 cases concomitantly harbored a deletion of the TP53 gene, as detected by FISH. Finally, we were interested in the analysis of CCND1, which is located on 11q13, a region frequently involved in chromosomal translocations (9/20 IgH translocated cases in our cohort). Here, we were able to detect 2/41 (4.8%) CCND1 mutated cases. Concerning the correlation between IgH rearrangements and molecular aberrations we observed that 21.9% (9/35; n=5 IgH status not available) of patients that were IgH rearranged, concomitantly carried a TP53 or RAS-RAF mutation. In more detail, 2/5 TP53 mutated patients and 50.0% (8/16) RAS-RAF mutated cases concomitantly harbored an IgH rearrangement. Taken together, MM patients are currently stratified in part based on cytogenetic/FISH classification. We demonstrated that deep-sequencing analyses support an additional molecular characterization. In our cohort, all patients carried mutations detected by FISH and 23/41 (56.1%) carried a molecular mutation. Future clinical studies need to confirm the frequencies of these mutations as well as their association with response to therapy and outcome. Disclosures: Artusi: MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kohlmann:MLL Munich Leukemia Laboratory: Employment. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Grossmann:MLL Munich Leukemia Laboratory: Employment.

Abstract Failure to achieve complete donor T-cell chimerism in the early post-transplant period may impair the graft-versus malignancy effect and contribute to a higher risk of disease relapse. Lineage specific chimerism analysis (LSCA) of T-lymphocytes versus myeloid cells is routinely performed following reduced-intensity or non-myeloablative hematopoietic cell allografts. However, many centers do not perform LSCA following myeloablative conditioning. We performed LSCA in all consecutive patients receiving a first T-replete allotransplant using myeloablative conditioning as defined by the CIBMTR (Giralt 2009 BBMT 15;367) between 2/2006 and 2/2015 (n=232; patient characteristics: median age 49 (18-73); Female 50%, Black 19%, Asian 3% white 78%; donor MRD 41%, MUD 39%, Haploidentical 20%; graft PBSC 88%, BM 11.5%, both 0.5%; diagnosis AML 52%, ALL 18%, MDS 11%, CML 11%, NHL 4% MPS1%, CLL1%, HL 1%; DRI low 10%, intermediate 51%, high 29%, very high 10%. Our objectives were to determine the rate of full-donor and mixed T-cell chimerism in the early post-transplant period (d 30 and d 90) , to determine the association of T-cell chimerism with patient, disease and regimen specific factors, and to determine whether early mixed chimerism impacts post-transplant outcomes following myeloablative conditioning. LSCA was performed on peripheral blood using a RoboSep instrument for automated sorting of CD3-positive T-cells and CD33 positive myeloid cells to 96.5-100% and 98.3-100% purity respectively, and short tandem repeat analysis by PCR. Full donor chimerism was defined as > 90% donor derived cells. Probability of achieving full donor T-cell chimerism in evaluable patients on d 30 and d 90 post transplant were 55% and 71%. In contrast the probabilities of achieving full-donor myeloid chimerism were 99.5% and 93.5% respectively. On univariate analysis the following factors were significantly associated with achievement of full-donor T-cell chimerism on d 30: donor type (haploidentical 100%, MRD 39%, MUD 47%, p<0.001), diagnosis (AML 58%, ALL 60%, MDS/MPS/CML 35%, NHL/HL/CLL 90%, p=0.003), conditioning regimen (busulfan based 37%, TBI based 56%, post-transplant Cy 100%, p<0.001), and on d 90: donor type (haploidentical 100%, MRD 56%, MUD 70%, p<0.001), diagnosis (AML 76%, ALL 77%, MDS/MPS/CML 49%%, NHL/HL/CLL 100%, p=0.002), conditioning regimen (busulfan based 56%, TBI based 77%, post-transplant Cy 100%, p<0.001). For multivariate analysis (Table 1) the exact logistic regression method was used to accommodate the nature of the data. The odds ratios (OR) were estimated by exponentiating median unbiased estimates of regression coefficients. Donor type (haploidentical vs other) and diagnosis (MDS/MPS vs AML) were significant factors. Our institutional approach to patients failing to achieve full-donor chimerism by d 90 is to withdraw immnosuppressive therapy in the absence of active GVHD if T-cell chimerism is > 50% or to administer DLI (starting at 1 x 10e6 CD3+ cells/kg) for patients with CD3 chimerism < 50% . Using this approach long term outcomes for patients who failed to achieve full-donor CD3 chimerism by d 30 were not significantly different from those achieving this threshold (2 yr estimated survival -81% vs 67%; disease-free survival 62%% vs 57%; 6 month CI of grade 2-4 acute GVHD 33% vs 41%; 2 yr CI of non-relapse mortality 9% vs 15%, relapse 29% vs 28%; p=NS for all). These data demonstrate that failure to induce early full-donor T-cell chimerism is relatively common following myeloablative allotransplantation using HLA-matched but not haploidentical donors. Although spontaneous improvement can occur, routine LSCA followed by immunologic manipulation of patients with suboptimal chimerism may assist in preventing adverse long term outcomes. Table 1. D 30 - OR = odds ratio of achieving full-donor CD3 chimerism Factor Effect OR 95% CI P value Diagnosis ALL vs AML 0.98 0.36 - 2.65 1.000 MDS/MPS/CML vs AML 0.37 0.13 - 0.94 0.035 NHL/HD/CLL vs AML 5.22 0.48 - 270 0.255 DonorType MRD vs MUD 0.63 0.29 - 1.35 0.266 Haploidentical vs MUD 57.3 12.0 - ∞ <0.001 Table 2. OR = odds ratio of achieving full-donor CD3 chimerism Factor Effect OR 95% CI P value Diagnosis ALL vs AML 0.70 0.20 - 2.49 0.719 MDS/MPS/CML vs AML 0.15 0.04 - 0.48 <0.001 NHL/HD/CLL vs AML 3.87 0.68 - ∞ 0.216 Donor Type MRD vs MUD 0.35 0.12 - 0.98 0.045 Haplo vs MUD 19.0 3.64 - ∞ <0.001 Disclosures No relevant conflicts of interest to declare.

Abstract Introduction Although fusion genes other than the immunoglobulin (Ig) translocation, t(4;14), which results in IGH-WHSC1fusions, are not frequently detected in multiple myeloma (MM), recent evidence suggests that kinase fusion gene fusions do occur relatively frequently and may inform on treatment algorithms. Here we use a hybrid-capture based, next-generation sequencing assay to survey fusion genes in patients with MM. Methods We report on 1421 samples from 958 individuals diagnosed with either monoclonal gammopathy of unknown significance (MGUS), smoldering multiple myeloma (SMM) or MM who underwent targeted sequencing with the FoundationOne Heme® (F1H) assay. Tumor samples were obtained from bone marrow aspirates, enriched by CD138+selection using magnetic beads (AutoMACs, Miltenyi Biotech, Cologne, Germany or RoboSep, StemCell Technologies, Vancouver, Canada). RNA and DNA were extracted using the AllPrep DNA/RNA mini kit (Qiagen, Hilden, Germany), RNeasy RNA extraction kit (Qiagen) or Puregene DNA extraction kit (Qiagen). ≥ 50 ng of extracted DNA or RNA was processed on the F1H assay. The current assay analyzes the complete coding DNA sequence of 405 genes, as well as selected introns of 31 genes involved in chromosomal rearrangements as well as the RNA sequence of 265 commonly rearranged genes resulting in gene fusions. Genes included in this assay encode known or likely targets of therapy, either approved or in clinical trials, or are otherwise known drivers of oncogenesis. Sequencing was to an average depth of 510x and was performed using the Illumina HiSeq 2500. Sequences were analyzed for selected gene rearrangements including fusion genes which were detected by a combination of DNA and RNA sequencing. Results Rearrangements into the Ig loci were detected and included the 5 main translocations: t(4;14), t(6;14), t(11;14), t(14;16), and t(14;20), as well as translocations involving MYC at 8q24. From a combination of DNA capture and RNA-seq expression values we used 107 samples in a training set with matching gene expression profiling data to determine cut-offs for FGFR3, WHSC1, CCND3, MAF, MAFB, CCND2 and CCND1to stratify patients into the 5 main translocation groups. We used these values to classify a further 391 samples with corresponding gene expression profiling (GEP) data, resulting in sensitivities and specificities of t(4;14), 98% and 100%; t(6;14), 100% and 99%; t(11;14), 99% and 95%; t(14;16), 77% and 100%; t(14;20), 100% and 100%, respectively. 40 non-Ig rearrangements were detected in 38 patients (4.2%), of which 21 in-frame fusion genes were predicted. Recurrent fusion-genes, identified in more than one patient, included EIF4E3-FOXP1, TXNDC5-MYC and SUB1-WHSC1. As well as TXNDC5, MYC was also partnered with FOXO3, both of which are known partners of the MYC translocation. 12 of the 21 in-frame fusion genes involved kinase domains, including fusions with BRAF (n=4), NTRK3 (n=2), ALK (n=1), ROS1 (n=1), MAPK14 (n=1), MAP3K14 (n=1), FGFR1 (n=1), and DLG2 (n=1). Fusions involving each of these genes have been documented in other cancers. BRAF fusions are thought to partner with genes encoding homodimerization domains, resulting in downstream activation of Ras signaling. Other kinase fusions result in receptor signaling and downstream activation of the Ras signaling pathway. Of the patients with kinase fusions, 2 had an activating KRAS, NRAS or BRAF mutation but only one was clonal (84% cancer clonal fraction). One patient with samples taken at different timepoints had a GTF2I-BRAF fusion and concomitant KRAS G13C mutation (16% allele frequency), both of which were not detectable 8 months later but an AGK-BRAF fusion was detected at that time suggesting clonal selection. Conclusion Non-Ig fusion genes are present in myeloma patients, but at a low frequency. Most of the fusions detected contained a kinase domain indicating activation of the Ras signaling pathway, which is also activated through KRAS, NRAS and BRAF mutations in 50% of patients. Although rare (1%), these kinase fusions are potential clinical targets in myeloma where kinase inhibitors, such as crizotinib, can be used which has shown to be effective against ALK and ROS1 fusions. Disclosures He: Foundation Medicine, Inc: Employment, Equity Ownership. Zhong:foundation medicine: Employment. Bailey:Foundation Medicine, Inc: Employment, Equity Ownership. Vergillo:Foundation Medicine, Inc: Employment. Ross:Foundation Medicine, Inc: Employment. Miller:Foundation Medicine: Employment, Equity Ownership. Stephens:Foundation Medicine: Employment, Equity Ownership. Mughal:Foundation Medicine: Employment, Equity Ownership. Davies:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Janssen: Research Funding; Univ of AR for Medical Sciences: Employment; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria.

Abstract Abstract 196 Until recent data, MM concept implies that all clones are linearly related to each other and homogenous in their mutational landscape. However, studies are now contradicting this model and reveal a more complex clonal architecture of Darwinian-like somatic evolution, where tumor progression proceeds in a branching rather than in a linear manner, leading to substantial clonal diversity and coexistence of wide genetic heterogeneity. By use of serial genomic analysis at different points during the disease course of MM patients, Keats et al. found the existence of 3 temporal tumor types, which can either be genetically stable, linearly evolving, or heterogeneous clonal mixtures with shifting predominant clones. In order to confirm these data we study on a large cohort of MM patients the emergence or disappearence by FISH analysis of t(4;14) and t(11;14) between diagnosis and relapse. We selected 444 patients from the IFM cell collection for whom we had a FISH analysis at diagnosis and relapse. Among them, 342 were evaluable for proceeding to FISH analysis. Upon receipt, bone marrow plasma cells were sorted using nanobeads and an anti-CD138 antibody (RoboSep, Stem Cell Technologies). After immuno-magnetic sorting, the plasma cell suspension purity was verified, and only samples with at least 90% of plasma cells were kept. Cells were then fixed in Carnoy's fixative. To test plasma cells for the t(4;14) and t(11;14), we did use specific IGH-FGFR3 and IGH-CCND1 fusion probes (Abbott Molecular). Hybridizations were performed according to the manufacturer's instructions. For analysis, at least 100 plasma cells with correct signals were scored using a Zeiss epifluorescence microscope. Our population baseline data presents usual characteristics: median age at diagnosis was 57 years (36y to 82y), diagnosis was made between 18/05/2000 and 19/08/2008. Relapse occurred between 11/08/2000 and 04/02/2009, with a median PFS of 26.6 months. The t(4;14) was present at diagnosis in 16.7% of the patients (38/232), and 11% (36/322) at relapse; Chi2 test did not find statistical difference between incidence at diagnosis and relapse (p=0.12). The t(11;14) was present in 24.6% of patients at diagnosis (48/195) but only 10.7% (20/187) at relapse (p=0.002). The purpose of our study was to explore clonal evolution during myeloma course. The t(4;14) translocation appeared (negative at diagnosis and positive at relapse) in 13 patients (n=218; 5.96%). On the contrary, t(4;14) disappeared in 11 cases (5.04%, n=218). In the same way, t(11;14) appeared for only 2 patients (1.42%, n=141) and disappeared in six cases (4.25%, n=141). Interestingly, we did not see switch between emergence and disappearance of the two translocations; no patient changed his cytogenetic status for one translocation to the other one. This phenomenon represents an important percentage of patients: for t(4;14), 11% of patients changed their status and 5.67% for t(11;14). Our data are in link with a study by Keats et al. who identified an evolution of aCGH data on a cohort of 28 patients showing changes over time for all their patients. Even if our data did not identify one of the three temporal tumor types described by Keats, the diversity of our findings (gain or loss of t(11;14) or t(4;14) between diagnosis and relapse) is an illustration on a large cohort of the clonal diversity and evolution of MM. Conclusion: this study describes for the first time on a large cohort of patients an aspect of subclonal evolution of MM. We identified a change of cytogenetic status for 11% of t(4–14) and 5,67% of t(11–14). These data illustrate the subclonal evolution of MM and underline the importance to perform novel cytogenetic analysis during disease course because treatment may be influenced by clonal expansion. Disclosures: Hulin: celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; janssen: Membership on an entity's Board of Directors or advisory committees. Kolb:janssen: Honoraria; celgene: Honoraria. Facon:onyx: Membership on an entity's Board of Directors or advisory committees; celgene: Membership on an entity's Board of Directors or advisory committees; janssen: Membership on an entity's Board of Directors or advisory committees; millenium: Membership on an entity's Board of Directors or advisory committees. Attal:celgene: Membership on an entity's Board of Directors or advisory committees; janssen: Membership on an entity's Board of Directors or advisory committees.