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Background: Acute myeloid leukemia (AML) is a genetically and clinically heterogeneous disease, characterized by expansion of undifferentiated myeloid precursor cells. The outcome for AML has improved through optimal treatment protocols, new drugs, and better supportive care; however, relapse remains common and patients with relapsed AML have poor prognosis. Recent genome-wide analyses revealed several recurrently mutated genes in AML, however, few of these driver mutations have been developed as therapeutic targets to date. In AML, t(8;21) and MLL (KMT2A) rearrangements are among the most frequent chromosomal abnormalities; however, knowledge of the genetic landscape is limited. Patients and Methods: The AML-05 study is a Japanese nationwide multi-institutional study of children (age < 18 years old) with de novo AML, conducted by the Japanese Pediatric Leukemia/Lymphoma Study Group (JPLSG). The trial was registered with the UMIN Clinical Trials Registry (UMIN-CTR; http://www.umin.ac.jp/ctr/index.htm; number UMIN000000511) and conducted in accordance with the principles set down in the Declaration of Helsinki, and approved by the Ethics Committees of all participating institutions. All patients, or their parents / guardians, provided written informed consent. For whole-exome sequencing (WES), whole-exome capture was accomplished by liquid phase hybridization of sonicated genomic DNA using a bait cRNA library (SureSelect Human ALL Exon V5 or V5 Inc RNA 5 kit), following the manufacturer's protocol. Massively parallel sequencing of the captured targets was performed using a HiSeq 2000/2500 (Illumina) with the paired-end 126-133 bp read option. For targeted sequencing, target enrichment was performed using a SureSelect custom kit (Agilent) designed to capture all coding exons of the 338 genes. Similarly, CCND1, CCND2, and CCND3 were also captured and sequenced in t(8;21) AML samples. For cell cycle analysis, cell lines were treated with DMSO, palbociclib (500 nM), or abemaciclib (500 nM) for 24 h. Then, cells were stained with propidium iodide and analyzed using a FACS Canto II flow cytometer (BD Biosciences). Results and Discussion: First, we analyzed paired AML tumor and germline samples from nine pediatric MLL-rearranged AML patients by WES. In total, 52 mutations (mean, 5.8 mutations/patient) were identified, including known mutational targets in AML, such as FLT3, BRAF, SETD2, BCORL1, and WT1. Moreover, novel CCND3 mutation was detected in one patient. Next, we analyzed 56 samples from patients with pediatric MLL-rearranged AML enrolled in the JPLSG AML-05 study, using targeted sequencing. We selected 338 genes, among which were previously reported and putative driver genes, including CCND3, for targeted sequencing. We identified eight mutations in CCND3 in five pediatric MLL-rearranged AML patients (8.9%). All mutations were clustered in the PEST domain. Four of the eight mutations were R271fs, which is a known hot-spot mutation in lymphoid malignancies. Mutations of the other D-type cyclins (CCND1, CCND2) have been reported in t(8;21) AML (Leukemia, 2017); therefore, we also searched for mutations in CCND1, CCND2, and CCND3 by targeted sequencing of samples from pediatric t(8;21) AML patients (n=105). CCND1 (n=3, 2.9%) and CCND2 (n=8, 7.6%) mutations were detected; however, no mutations of CCND3 were detected. By contrast, there were no mutations of CCND1 or CCND2 in MLL-rearranged AML (n=56), suggesting that mutations of D-type cyclins exhibit a subtype-specific pattern in AML. A recent study demonstrated that genomic aberrations that activate D-type cyclins are associated with enhanced sensitivity to the CDK4/6 inhibitor (Cancer Cell, 2017), therefore, we also examined the effects of CDK4/6 inhibitors (abemaciclib and palbociclib) on two t(8;21) AML cell lines (Kasumi-1 and SKNO-1) and five MLL-rearrangement AML cell lines (ML-2, MV4-11, MOLM-13, THP-1, and NOMO-1) were analyzed. All cell lines described above exhibited impaired proliferation after treatment with CDK4/6 inhibitors. Furthermore, treatment of these cell lines with CDK4/6 inhibitors resulted in detection of lower frequencies of S/G2/M phase cells by flow cytometry, suggesting that cells were arrested in G1 phase via CDK4/6 inhibition. These data provide further insights into the genetic basis of, and potential therapeutic strategies in t(8;21) and MLL-rearranged acute myeloid leukemia. Disclosures No relevant conflicts of interest to declare.

The genomic landscapes of acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), myeloproliferative disorders (MPD) and other related myeloid malignancies are now amongst the best characterized cancer genomes. These malignancies share most of their somatic driver mutations, many of which have therapeutic and prognostic significance (Patel et al, NEJM 2012). Patient prognostication and clinical decision-making can be greatly facilitated by testing for these mutations in parallel with established diagnostic assays. Here, we describe and validate RISCL (Rearrangements, Indels, Substitutions, Copy number and Loss-of-heterozygosity), a novel methodological and bioinformatic tool for the molecular diagnosis of myeloid malignancies. This tool employs targeted DNA capture to simultaneously: 1) identify coding mutations in 49 genes, 2) detect the four most important translocations in AML and 3) derive genome-wide copy number and zygosity data. Samples & methods 1. Samples Genomic DNA was extracted from bone marrow samples of 62 patients with AML (n=86 samples, including 24 remission samples) and 68 patients with MDS; and from blood granulocytes and mononuclear cells from 5 cord blood samples and 18 adults with normal hematopoiesis. 2. cRNA baits and sequencing The bait library (Agilent) contained 53,613 probes to capture: 1) all exons from 49 genes 2) intronic breakpoint sites for PML-RARA, CBFb-MYH11 and RUNX1-RUNX1T1 and MLL breakpoints 3) 9958 SNPs (minor allele frequency 0.40-0.45) for genome wide copy number and zygosity analysis. Barcoded sequencing was performed using Illumina HiSeq 2000 (100bp paired-end). 3. Bioinformatic analysis We used bespoke bioinformatics for detecting coding substitutions and indels (MIDAS; Conte et al, Leukemia 2013), chromosomal translocations (SMALT-FIT), copy number analysis (Avadis software) and detection of specific mutations such as MLL-PTD and FLT3-ITD (in-house scripts). 4. Verification of results To validate the sensitivity and specificity of our approach, we compared our findings to conventional diagnostic data and are also validating 30% of randomly selected variants. Results A mean of 94% of targeted bases were covered at least by 30x. In AML samples, the four most common coding mutations identified affected NPM1 (n=10), CEBPA (n=8), IDH1 (n=8) and NRAS (n=7). By comparison to conventional diagnostics, we detected 5/5 IDH1R132, 4/4 CEBPA, 1/1 IDH2R172K and 8/9 NPM1 mutations. In MDS samples, the top four mutations affected TP53 (n=15), TET2 (n=13), SRSF2 (n=9), ASXL1 (n=8) and mutations affecting spliceosome genes (n=18) that were mutually exclusive, as previously described (Yoshida et al, Nature 2011). RISCL detected 100% of known translocations (28/28) in AML patients, namely CBFb-MYH11 (n=8/8), PML-RARA (n=9/9), RUNX1-RUNX1T1 (n=4/4) and rearrangements of MLL (n=7/7). In every case of MLL rearrangement the gene partner was identified and in one case with t(X;11) we identified a novel gene partner to MLL, DIAPH2. Furthermore, we identified one patient with an MLL rearrangement not identified at diagnosis. Copy number analysis efficiently detected known large chromosomal deletions or monosomies in chromosome 5 (18/18) and 7 (10/12). Overall 47/54 large deletions were detected using Avadis software. Furthermore in one MDS patient we were able to detect a submicroscopic heterozygous deletion in chromosome 4 which included TET2. However this method was much less sensitive for detecting trisomies (13/27 trisomies detected overall). The reasons for this disparity between detection of deletions and amplifications using a standardized depth of coverage algorithm are unclear, but may include subclonal mutations, selection of karyotypically abnormal cells during metaphase preparation or limitations of our bioinformatic analysis, which we are currently investigating. Figure 1 shows an example of the results of our holistic analysis using RISCL from an informative case of AML. In summary we describe RISCL, a novel powerful holistic NGS tool for detailed characterization of myeloid malignancies that can be used for patient stratification and a personalized approach to malignancy in the molecular era. The same approach can be extended to other malignancies. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

The standard approach to multiple myeloma relies on marrow morphology, serum and urine biochemistry and skeletal imaging for diagnosis. Conventional karyotyping and FISH are performed to assess prognosis but with limited impact on therapeutic decisions. Recently, several recurrent mutated genes have been described, some of which are clinically actionable, but their prognostic value remains to be prospectively investigated. Therefore, strategies to investigate the landscape of chromosomal and gene lesions of a large number of myeloma samples in a robust fashion will soon be needed. In this study, we developed a target-enrichment strategy to streamline simultaneous analysis of gene mutations, copy number changes and IGH translocations in multiple myeloma in a high-throughput fashion. We designed Agilent SureSelect cRNA pull down baits to target 246 genes implicated in myeloma and/or cancer, 2538 single nucleotide polymorphisms to detect copy number and allelic ratio at the single-gene and whole-genome level, and we tiled the whole IGH locus to detect IGH translocations and V(D)J rearrangements. As a pilot, we sequenced 13 myeloma cell lines and 10 control haematopoietic cells lines in HiSeq2000 with 75-bp paired end protocol and used standard algorithms developed at the Sanger Institute for analysis. With a mean coverage of the target region of 130x we identified 530 likely oncogenic substitutions and indels, 98.7% of which were validated as real by independent whole exome sequencing. Mutation spectrum of myeloma cell lines was different from patient samples, with mutations in PCLO being the most frequent (8/13) followed by TP53 (7/13), NEB and LTB (6/13). Other genes canonically mutated in myeloma (KRAS, NRAS, BRAF, DIS3, FAM46C) were found at lower frequencies. As expected in a cell line study, most mutations were fully clonal. Using normalized coverage data we were able to study genes and regions showing recurrent copy number changes. With this approach we identified losses in CDKN2C, FAM46C, BIRC2, BIRC3, RB1, DIS3, TRAF3, CYLD and TP53. Furthermore, we could detect deletions in 1p32.3, 1p12, 12p13.31 16q12 and 17p13, as well as amplifications in 1q21.1 and 1q23.3. Validation by SNP-array confirmed an overall positive predictive value of 98% for sequencing data with regards to gain and losses of genes and small chromosomal regions. Analysis of read pairs where mates are aligned to different chromosomes allowed identification and near-bp mapping of the t(11;14) and t(8;14) translocations in 3 cases each, and t(4;14) in two cases, always consistent with the published karyotype for the cell line. No events were found in control cell lines, but our algorithm missed a t(4;14) and a cryptic t(11;14) in two cell lines. Last, we identified reads spanning the V(D)J rearrangement site in most myeloma cell lines, and we are developing tools to reconstruct the tumor-specific rearrangement sequence that could be employed in minimal residual disease monitoring in the future. This panel is now being applied to a cohort of 500 myeloma patient samples with clinical annotation for prognostication. In conclusion, we describe sequencing techniques and analysis tools that can be easily implemented in diagnostic and research laboratories and can be deployed in the study of myeloma pathogenesis, diagnosis, prognosis and as minimal residual disease detection. Disclosures Ganly: Medipics: Equity Ownership; Bone Marrow Cancer Trust, New Zealand: Membership on an entity's Board of Directors or advisory committees. Campbell:14M Genomics Limited: Consultancy, Equity Ownership.

Abstract 3547 Introduction: Recurrent somatic mutations are valuable prognostic markers in cytogenetically normal Acute Myeloid Leukemia (AML). The most common of these mutations is a 9 to ∼150 bp internal tandem duplication (ITD) in the fms-related tyrosine kinase 3 (FLT3) gene, which is typically identified via PCR amplification and capillary electrophoresis. Since testing for individual mutations in this manner will become laborious and expensive as the number of clinically relevant mutations increases, we and others have proposed using targeted next-generation sequencing (NGS) for comprehensive detection of somatic mutations in multiple genes simultaneously. Successful application of this approach will require automated analysis methods capable of sensitive detection of a variety of mutation types, including single-base substitutions and insertions/deletions, with a low false-positive rate. However, the accuracy of current methods for identifying medium-sized insertions such as the FLT3 ITDs has not been established. Therefore, we sought to determine the ability of several common analysis tools to identify FLT3 ITDs from Illumina NGS sequence data. Methods: We performed targeted sequencing of 10 samples with known FLT3 ITDs ranging between 17 and 93 base-pairs (bp) as part of a larger test panel of 28 genes commonly mutated in AML and other malignancies. Nine of the FLT3 ITD-positive samples were from patients with newly diagnosed AML and were confirmed by PCR and capillary electrophoresis. A cancer cell line known to be heterozygous for a 30 bp FLT3 ITD, MV4-11, was also included. Indexed Illumina sequencing libraries were generated using automated library preparation and enriched for target regions using solution-phase hybridization-capture with biotinylated cRNA probes targeting exons +/− 200 bp plus 1 kb flanking the FLT3 gene and the 27 other genes in the panel. Enriched libraries were sequenced in multiplex on an Illumina HiSeq instrument using 2 × 101 bp reads. Demultiplexed reads were mapped to the hg19 reference sequence with novoalign, and indels were called in a 1 kilobase-pair region surrounding the FLT3 ITD with samtools, GATK, maq, CLC Genomics Workbench, PINDEL, and DINDEL using default parameters, in addition to de novo assembly of reads with partial similarity to the region using phrap. Insertion calls were then compared to results from PCR and capillary electrophoresis. Results: Multiplex sequencing resulted in 585 to 1,000-fold raw coverage of the FLT3 gene for the 10 study samples (Table 1). No FLT3 ITD insertions were detected in any sample using the common NGS analysis tools samtools, GATK, maq, DINDEL, and CLC Genomics Workbench. However, PINDEL identified insertions between 17 and 72 bp in 9 of 10 FLT3 ITD-positive samples. PINDEL failed to detect a 93 bp ITD insertion (the largest insertion in this set) in one patient sample, as well as an 84 bp insertion in a patient with two insertions (81 and 54 bp) detected by standard methods. De novo assembly of the FLT3 ITD region also resulted in detection of insertions in 9 of the 10 cases. No insertions were called in an additional set of 15 samples without known FLT3 ITDs. Conclusions: We evaluated the ability of several NGS analysis tools to detect previously known FLT3 ITDs in multi-gene targeted NGS data. Most of the general-purpose analysis tools we tested were unable to detect FLT3 ITD insertions. However, two approaches detected known FLT3 ITD insertions in 90% of the samples tested in this study, including the program PINDEL and de-novo assembly of the FLT3 ITD region using phrap. These results demonstrate that medium-sized FLT3 ITD insertions can be detected in clinical samples by high coverage NGS sequencing with the appropriate analysis pipeline. However, further methods for reliable detection of larger (>70bp) insertions must be developed before clinical NGS-based methods can be applied to the detection of the full spectrum of somatic mutations present in leukemias and other malignancies. Disclosures: No relevant conflicts of interest to declare.

Abstract 163 Iron plays a key role as a cofactor in many fundamental metabolic processes, which require heme synthesis and Fe/S cluster assembly in the mitochondria. Defects in the transport of iron into the mitochondria would lead to anemias due to a deficiency in heme and hemoglobin synthesis. Here we describe a zebrafish genetic mutant, pinotage (pnttq209), which exhibits a profound hypochromic, microcytic anemia. Erythrocytes from pnt mutants have a defect in hemoglobinization and decreased red cell indices (mean corpuscular volume and hemoglobin content, hematocrit, hemoglobin concentration). Through positional cloning, we showed that the mitochondrial ATPase Inhibitory Factor 1 (atpif1), which regulates the inner mitochondrial membrane potential, is the gene disrupted in pnt. The identity of the pnt gene was verified by: (a) decreased atpif1 steady-state mRNA in pnt mutants, (b) phenocopying the anemia with anti-sense atpif1 morpholinos, (c) functional complementation of the anemia with atpif1 cRNA, and (d) a genetic polymorphism in the 3'UTR co-segregating with the mutant phenotype that destabilizes the atpif1 mRNA. Consistent with the conserved function of atpif1 in higher vertebrates, the silencing of the murine ortholog of atpif1 in Friend mouse erythroleukemia (MEL) cells showed a defect in hemoglobinization by o-dianisidine staining and reduction of 59Fe incorporation into heme in 59Fe-metabolically labeled cells. Moreover, Atpif1 knockdown destabilizes their mitochondrial membrane potential and volume. Therefore, the identification of atpif1 in pnt functionally demonstrates the role of atpif1 in regulating the proton motive gradient across the inner mitochondrial membrane for mitochondrial iron incorporation in heme biosynthesis. These results uncover a novel hematopoiesis-related function of atpif1, which will directly contribute to our understanding and potential treatment of human congenital and acquired anemias. Disclosures: No relevant conflicts of interest to declare.

Abstract 1658 Prognostic evaluation is a critical step in newly diagnosed pts with AML. With samples containing a high leukemic blast load, microarray-based gene expression profiling (GEP) and class prediction analyses have demonstrated their ability to assign AML samples to one of the three well characterized favorable-risk AML subtypes, ie, APL, CBFA-AMLs, CBFB-AMLs, with high accuracy and low error rates. One major issue regarding the use of microarray-based GEP in a routine prognostic workflow, concerns its ability to perform accurate class prediction analyses with samples containing as low as 20% blasts. So far, the minimum leukemic cell load that would still lead to correct class assignment has not been extensively studied. Another critical issue for a routine use of microarray-based GEP concerns samples that do not fulfill all quality control criteria along the process. The accuracy of class prediction analyses using data derived from poor quality AML samples also deserves further evaluations. In this study, using the Illumina BeadChip technology, GEP was first assessed for its ability to correctly classify APLs, CBFA-AMLs, CBFB-AMLs within a training set of 72 samples containing at least 60% of leukemic cells (20 APL samples from 14 pts, 12 CBFA-AML samples - n=6 pts, 11 CBFB-AML samples - n=10 pts). This training set also included normal bone marrow samples as well as a more heterogeneous group of 29 NK-AML samples (28 pts). The classifiers derived from this supervised analysis were then evaluated for their ability to assign to the correct class 93 test samples with either low leukemic blast load (as low as 5% of blasts – n=79 - 22 samples that originally contained less than 60% of leukemic cells, 57 samples with high blast load that were artificially diluted within a pool of normal bone marrows at 50 and 75%, ie, final leukemic blast load after dilution ranging from 10 to 50%), poor quality control criteria (n=10), or with atypical characteristics (n=4). The Class Prediction Module of ArrayMiner 5.3.3 software, which is a supervised method based on a proprietary optimization technique called grouping genetic algorithms (GGA), was used to build the classifiers associated with each class of the Training Set and assess the predictive capacity of these classifiers on Test Set samples. Using the Training Set and the GGA-base supervised method, the best model fitness was achieved with classifiers including 14 markers per class. With these classifiers, all Training Set samples were assigned to the correct class. Considering Test Set samples, all APL and CBFA-AML samples were assigned to the correct class, including the ones with the lowest leukemic blast load, i.e., 7% for APLs and 15% for CBFA-AMLs. One of the 23 CBFB-AML Test Set samples, which contained 5% of blasts was misclassified. All other CBFB-AML test samples were correctly classified, even those contained as low as 10% of leukemic cells. All but one of the NK-AML test samples were assigned to the correct class, even though some contained as low as 19% of leukemic cells. Concerning the 10 poor quality control samples characterized by either low RIN values and degraded total RNAs, low amounts of labeled cRNAs or both low RIN value and low labeled cRNA, all were assigned to the expected class. All atypical AMLs were assigned, as expected, to the NK-AML group. In conclusion, favorable cytogenetic risk AMLs with low leukemic cell load or poor quality control criteria can be correctly assign to the appropriate group using GEP. Transposability of this Illumina technology-based model and its efficiency in term of predictive capacity, will be presented using an independent series of 245 AML samples processed at a different institution, on Affymetrix GeneChips. Disclosures: No relevant conflicts of interest to declare.

Abstract 2040 The erythroid Rh family of proteins includes RhCE and RhD which carry the common Rh antigens, and the related Rh-associated glycoprotein, RhAG. RhAG is required for trafficking of the blood group proteins to the membrane and forms the core of a macro-complex in the membrane which includes glycophorin B, Band 3, CD47, and LW. The Rh proteins are structurally and functionally related to the Amt superfamily of NH3/NH4+ transport proteins, and RhAG and its nonerythroid paralogs, RhCG and RhBG, have been shown to mediate NH3/NH4+ transport. RhCG is responsible for part of renal collecting duct epithelial cell NH3/NH4+ secretion, and Rhcg-/- mice exhibit incomplete distal renal tubular acidosis due to impaired urinary NH4+ excretion. The Rhag-/- mouse is grossly normal, and the significance of RhAG-mediated NH3/NH4+ transport in human erythrocytes remains unclear. Over-hydrated hereditary stomatocytosis (OHSt) is a rare dominant disorder characterized by moderate hemolytic anemia, increased mean red cell volumes, stomatocytes and echinocytes, and increased red cell permeability to the monovalent cations, Na+ and K+. Six of the seven OHSt kindred studied by Bruce et al. (Blood. 2009;113:1350) displayed a heterozygous Phe65Ser mutation in RhAG. Expression studies of the mutant 65Ser-RhAG in Xenopus oocytes induced a monovalent cation flux compatible with the cation leak seen in RBCs. The increased Na+ and decreased K+ contents of mutant RhAG-expressing oocytes suggested that F65S is a gain-of-function mutation that opens a cation leak, likely within the RhAG polypeptide. In this study the ammonia transport properties of the OHSt mutant 65Ser-RhAG were investigated. Xenopus oocytes were injected with cRNA encoding wild-type RhAG, the OHSt mutant 65Ser-RhAG, and 65Val-RhAG, an engineered mutation with a smaller hydrophobic side chain at position 65. Wild-type and mutant RhAG polypeptides were well-expressed in the oocyte membrane as measured by quantitative immunoblotting. Uptake of the NH3/NH4+ substrate analog 14C-methylammonium (MA), was assayed in oocytes previously injected with water (control) or with cRNA. Expression of wild-type RhAG mediated MA uptake at rates 6-fold greater than that of water-injected controls. Uptake of MA by oocytes expressing 65Val-RhAG was equivalent to that of wild type RhAG. However, MA uptake by oocytes expressing OHSt mutant 65Ser-RhAG was greatly reduced to less than 20% that of oocytes expressing wild-type RHAG or 65Val-RhAG, and was only 1.5-fold greater than that of water-injected control oocytes. Co-expression with other, individual Rh complex members glycophorin B, RhD, RhCE, or Band 3 did not alter MA-mediated uptake by RhAG-expressing oocytes. Importantly, this study reveals that the RhAG mutation Phe65Ser found in patients with type 1 over-hydrated stomatocytosis is a loss of function mutation. Further study is required to define the relationship between loss of NH3/NH4+ transport and erythrocyte Na+ and K+ cation content. Disclosures: Westhoff: Immucor: Scientific Advisor.

Abstract 2523 Poster Board II-500 Prostaglandin E2 (PGE2), a member of the eicosanoid family, is a local signaling molecule released from mammalian blood vessel walls and other hematopoietic niches. PGE2 produces tissue specific effects by interacting with G protein receptors in cell membranes. It has been found to be an important regulator of hematopoietic stem cell (HSC) number during embryogenesis and is required for the development of HSCs (North et al., Nature 447:1007–1011, 2007). Additionally, PGE2 enhances HSC engraftment in competitive transplantation assays (North et al., Nature 447:1007–1011, 2007) through alterations in the survival, proliferation and homing of transplanted cells (Hoggatt et al., Blood 113:5444–5455, 2009). Immunoselected CD34+ cells were isolated from the leukapheresis products of both human and non-human primates following cytokine mobilization with G-CSF. CD34+ cells were incubated in X-Vivo 10™ with either 50μM or no PGE2 for one hour at 37°C on RetroNectin™ coated plates. After one hour incubation, media was replaced with X-Vivo 10™ supplemented with 10ng/mL SCF and IL-6 and cells were kept at 37°C and collected either prior to or at 2, 6, 12 and 24 hours following PGE2 exposure. Total RNA was isolated from cells and amplified to cRNA. Control cRNA was labeled with Cy3 dye and experimental cRNA was labeled with Cy5 dye. Control and experimental labeled cRNA was co-hybridized to a custom-made 17.5K cDNA (UniGene cluster) microarray. The relationship between the cells was analyzed using an unsupervised Eisen's hierarchical clustering method and gene regulation was analyzed at each time point using an Array scatter plot comparison. Statistical analysis was done using Array Class Comparison analysis and pathway analysis was carried out using Ingenuity Pathway Analysis. Many of the significantly up regulated genes were associated with pathways activated by PGE2, including genes involved in the inflammatory response, signal transduction, and G proteins. Interestingly the human CD34+ cells showed at 2 hours a 21.8-fold increase of 3′,5′-cyclic AMP phosphodiesterase mRNA consistent with prior observations in zebrafish and murine cells (Goessling et al. Cell 136: 1136–1148). Pathways that increased at 2 hours include molecular mechanisms of cancer, and those that were down regulated include CCR5, CTLA4, and leukocyte extravasation signaling. At 6 hours the molecular mechanisms of cancer pathway predominated even more (diminishing by 12 hours) while oxidative phosphorylation was down regulated. Genes involved in cAMP and WNT signaling are observed to be up regulated at earlier time points but diminish with time. These results correlate with protein expression observations that have been made in the zebrafish and murine models following PGE2 treatment (Goessling et al. Cell 136: 1136–1148) and are consistent with the observation that PGE2 may influence the self renewal of HSCs. Disclosures: Goessling: Fate Therapeutics: Consultancy, Patents & Royalties.

Myelodysplatic syndrome (MDS) represents a heterogeneous group of clonal disorders with ineffective hematopoiesis that is characterized by dysplasia and peripheral cytopenia of one or more cell lineages. We studied gene expression profiles in CD34+ cells of 42 MDS patients and 6 healthy controls using Illumina cDNA microarray technology. Nine patients had RA, 7 patients had RCMD, 17 patients had RAEB and 9 had RAEB-T. CD34+ cells were isolated from bone marrow samples using MACS magnetic columns. The quality of total extracted RNA was confirmed with the Agilent Bioanalyzer 2100. 200ng of total RNA was amplified using Illumina RNA amplification kit. cRNA targets were hybridized on the Sentrix HumanRef-8 BeadChips (> 24 000 probes), which were scanned on the Illumina BeadStation 500. The data were pre-processed and normalized by lumi R package designed to preprocess the Illumina microarray data. Normalized data were filtered by detection p-value

Background: Patients with chronic phase CML who achieve a complete cytogenetic response (CCR) have a low risk of disease progression. Since patients unlikely to achieve CCR may benefit from more aggressive therapy up-front, prediction of response prior to therapy would be useful. Our previous work has demonstrated that the gene expression profiles of unselected leukemia cells from prospective cytogenetic responders and non-responders are similar (Crossman et al. Haematologica90(4):459–64, 2005). Based on the hypothesis that cytogenetic refractoriness may be a property of leukemic progenitor rather than differentiated cells, we explored gene expression profiling of CD34+ cells as a tool for predicting CCR. Methods: Two independent data sets were generated. The first data set (learning set) was based on patients with CML who had either achieved a CCR within 1 year of imatinib therapy (R, n = 24), or remained at least 65% Ph+ (NR, n = 12). The prospectively collected, completely independent validation data set was based on 23 additional subjects using the same criteria (17 R and 6 NR). For the learning set CD34+ cells were isolated by FACS from bone marrow or peripheral mononuclear cells cryopreserved prior to imatinib therapy. For the validation set CD34+ were isolated from fresh cells using immunomagnetic columns. RNA was extracted and samples with ≥5ng of high quality total RNA were amplified and labelled with the Affymetrix two cycle cDNA synthesis and IVT labeling protocol using Results: On the validation set, the classifier had an estimated accuracy rate of 85.7%. Based on transcript annotation analysis, there is significant over-representation of trancripts related to cell cycle, complement and coagulation, and apoptosis, among others. A functional cluster was identified related to regulation of transcription. Transcription factors up-regulated in cells from patients with subsequent cytogenetic refractoriness included ZNF168, MEIS1, KLF2, NFIB, MAF, FOSB and EGR1. In contrast, we found down-regulation of SERPINA1, THBD and PLAUR, a sub-network in the complement and coagulation cascades pathway that is associated with cell adhesion and migration. Conclusions: The transcriptional profiles of CD34+ cell from prospective cytogenetic responders and non-responders are distinct. A classifier based on the learning set predicted cytogenetic response in a totally independent validation set with ∼86% accuracy, making this one of the first prospectively validated classifiers. Examination of functional annotation for the transcripts in the classifier identified several functional clusters that are highly correlated with respect to direction of response (e.g. transcription factors) and may drive the biology of cytogenetic refractoriness.

Gene expression profiling has the potential to offer consistent objective diagnostic test results once a standardized protocol is established. We investigated the robustness, precision, and reproducibility of this technology and present data that complements the Microarray Innovations in LEukemia study (MILE study). In four laboratories, located in Germany (D), Austria (A), and Switzerland (CH) (DACH study), replicates of 112 patient samples were analyzed using the AmpliChip Leukemia research test. Patient samples were centrally collected and diagnosed in daily routine at the Munich Leukemia Laboratory and represented 8 distinct classes of acute and chronic leukemias, with non-leukemia as control group. After purification of the mononuclear cells by Ficoll density centrifugation, 4 × 5 million cells were frozen in lysis buffer and stored at −80°C. Equipped with identical instruments, software, and reagents, study operators were trained on the microarray sample preparation protocol using total RNA from commercially available cell lines. Upon receipt of the frozen lysates each of the four laboratories purified the total RNA from the 112 technical quadruplicates. 99.3% (445/448) of the sample preparations were successfully performed. On average, 8.4 μg, 7.2 μg, 7.4 μg, or 7.5 μg of total RNA, respectively, were isolated from the mononuclear cells from the four laboratories. In three samples less than 1.0 μg of total RNA was obtained and thus the preparation failed. Bland-Altman plots of agreement showed that any two centers were unlikely to have more than an 8.3 μg difference in yield of total RNA from the same sample. On average there was between 0.1 μg to 1.2 μg difference in total RNA yield from the same sample. Further processing of the 445 samples resulted in 437 (98.2%) successfully performed in vitro transcription reactions, i.e. obtained cRNA yield of >8.0 μg. On average there was between 0.4 μg to 7.4 μg difference in cRNA yield from the same sample. After hybridization to microarrays on average, 46.1%, 48.6%, 46.5%, and 47.3% of probe sets were detected as present with mean scaling factors of 4.3, 2.9, 3.9, and 3.7, respectively. The mean values and standard deviations of distributions of the coefficient of variation (CV) within each site over all the probe sets of the quantile normalized signals on the chip were 27.2% (StdDev: 12.3%), 27.0% (StdDev: 12.3%), 27.3% (StdDev: 12.3%), 26.9% (StdDev: 12.4%), respectively. Furthermore, in unsupervised hierarchical cluster and principal component analyses replicates from the same patient always clustered closely together, with no indications of association between gene expression profiles due to different operators or laboratories. In conclusion, we demonstrated that microarray analysis can be performed with remarkably high inter-laboratory reproducibility and with comparable quality and high technical precision across laboratories.

Aim: The gene expression analysis system is a good tool to identify biomarkers related to prognosis or response predictors of therapy in haematological malignancies. Low density microarrays are an useful technology to obtain genetic expression profiles in different neoplastic disorders. Recently we have designed an oligonucleotide biochip (Hematochip, Alvarez et al, Clin Chem2007;53:259) useful for study peripheral blood in hematological malignancies. Chronic Lymphoid Leukemia (CLL) is a frequent clonal lymphocyte disorder characterized by clinical diversity and that have known prognosis markers but a more accurate subclassification could be required. Our objective was to define CLL subgroups with differential genetic profile expression. Patients and Methods: Analytical, prospective study in 49 consecutive CLL patients diagnosed since January 2003 to December 2004 in the Hematology Department of Miguel Servet University Hospital. Zaragoza. Spain. At diagnosis demographic, clinical stage, analytical, immunophenotype expression, genetic aberrations and mutational status of IgVH were collected at electronically data base. Simultaneously, peripheral blood from 20 healthy donors and all CLL patients were collected in PAXgene tubes (PreAnalytix). cDNA was generated from total RNA and double-strand cDNA was used as a template to generate biotinylated cRNA by in vitro transcription using the MEGA-script T7 High Yield Transcription kit (Ambion). Fragmented cRNA was denatured and hybridized using a Ventana Discovery Station (Ventana Medical Systems). Arrays were stained with Cy3-conjugated streptavidin (Amersham Biosciences) and scanned using a ScanArray 4000 scanner (Perkin-Elmer). The data were treated and analyzed by GeneSpring clusters method. The expression profiles were compared and stratified with clinical stability or progression, gender, ZAP 70 expression, associated neoplasia, time free of therapy and response or not to Fludarabine. Results: The Hematochip has identified 9 probes obtained with filtered data and statistical significance (p Comments: the study of genetic profiles by Hematochip could be a useful analysis to predict the stability or progression of CLL patients at diagnosis. Further studies analyzing more cases will be necessary in order to validate these results. This work has been partially sponsorized by grants: Mutua Madrilena del Automovil, Fundacion para el Estudio de la Hematologia y Hemoterapia de Aragon and I+CS.

Gene expression profiling (GEP) is a powerful technology for the molecular analysis of leukemia and it groups biologically defined disease entities into distinct sub-classes that can provide diagnosis, guide therapy, and even correlate with disease prognosis. The experimental procedures of micorarray analysis are often cumbersome and provide ample opportunity for variability in gene expression data. We previously reported on our efforts to standardize micorarray analysis across 11 participating laboratories within the international MILE study (Microarray Innovations in LEukemia) where a large dataset of over 4,000 leukemia patient samples is being generated using both Affymetrix HG-U133 Plus 2.0 and custom format microarrays. For a better applicability in a routine laboratory workflow and in order to improve the robustness of the micorarray analysis we now have modified the original micorarray sample preparation protocols as originally published by the manufacturer. Here we report the final results of this effort to minimize the complexity of the sample preparation protocol and to reduce the time that is necessary to run the assay. We designed pre-assembled kits for total RNA preparation, nucleic acid cleanup, cDNA synthesis, in vitro transcription, hybridization and staining, and wash buffers guiding the operator through the whole process of sample preparation to microarray result generation. To further improve the ease of use of this assay we minimized to a large extent the overall complexity of sample amplification and labeling, as well as target hybridization and detection procedures. For example, for the RNA amplification, cRNA labeling, and signal detection process, the number of individual reagent vials was reduced from 32 to 13 vials. This was achieved by combining individual components to ready-to-use master mixes. Furthermore, starting from total RNA, the time required for generation of labeled and fragmented cRNA has been reduced to a convenient eight hour work-shift. Overall, compared to the original 48 hour protocol as recommended by the manufacturer the new workflow generates microarray data in 26 hours. In total, this development program included n=900 whole genome microarray tests. By comparison testing of the original and the final modified protocols on we further can demonstrate by squared correlation coefficients both high inter-assay (R2 > 0.9) and intra-assay (R2 > 0.9) reproducibility and precision of gene expression data of this new sample preparation method. Data from cell lines, normal bone marrow, as well as leukemia samples representing the subclasses AML with normal karyotype or other abnormalities, AML with complex aberrant karyotype, CML, and CLL indicate that reproducible subclassification of leukemias is feasible as all samples were predicted by a classification algorithm as the same class as when the samples were prepared according to the the original method. In conclusion, we developed a robust sample processing methodology for microarray analysis of leukemia samples that allows to generate standardized and reproducible microarray results in multiple laboratories.

The diagnosis of myelodysplastic syndrome (MDS) is made largely on the dysplastic morphology of BM cells from aspiration or biopsies. Prognosis scored by IPSS is depending on the percentage of marrow myeloblasts and the clonal cytogenetic abnormalities. To expand the understanding of genetic defects in hematopoietic cells of MDS in hope of finding novel genes correlated to pathogenesis and provide possible diagnostic marker for MDS, we have applied microarray to analyze the clinical samples from MDS patients. Total RNAs of CD34+ cells from 8 patients ( 2 RAEBt,2 RAEB,2 RA,1 RAS,1 CAA ) and one healthy people were extracted followed by a double in vitro transcription to circumvent the limited number of CD34+ cells. Following a modified Affymetrix target amplification protocol. Biotinylated cRNA was synthesized from 50 ng total RNA by double-round amplification and hybridized to an Human Genome U133 Plus 2.0 Array (Affymetrix). From the expression profile of 18404 different genes, we revealed that DNTT,MLL3,IL1R2,MAPK1,IGLL1 were down regulated while EGR-1, Rap1GAP or MAF were up regulated compared with normal controls. Most notably, Dlk1 was up regulated in MDS, while down regulated in AML and normal. By real-time RT-PCR we confirmed that in BMNCs the median levels of Dlk1 transcript in patients with RA and RAS were 2.55 (range, 0.00–23.7), RAEB and RAEBt were 8.24(range, 2.01–18.44), AML were 1.88 (range, 0.12–5.13), and other patients were 0.37(range, 0.00–1.79), respectively. The abundance of Dlk1 mRNA in MNCs from most MDS patients was markedly greater than that in the MNCs from others (P

Background: Patients with chronic phase CML who achieve a complete cytogenetic response (CCR) have a low risk of disease progression. Since patients unlikely to achieve a CCR may benefit from more aggressive therapy, it would be clinically advantageous to identify those patients prior to therapy. Based on the hypothesis that cytogenetic refractoriness may be a property of leukemic progenitor cells, we explored the potential of gene expression profiling of CD34+ cells as a tool for predicting failure to achieve CCR. Methods: Fifty-one patients with CML who either achieved a CCR within 1 year of imatinib therapy (R, n = 35), or remained at least 65% Ph+ (NR, n = 16) were included in the study. Pre-imatinib CD34+ cells were FACS isolated from cryopreserved bone marrow mononuclear cells. RNA was extracted from the CD34+ cells and samples with >=5ng of high quality total RNA were amplified and labelled with the Affymetrix two cycle cDNA synthesis and IVT labeling protocol using Results: The selection procedure yielded CD34+ cells with a median purity of 95.9% and median cell number of 1.0x104. Despite relatively low numbers of input cells, successful hybridization was achieved for 36 patients (24 R and 12 NR). Partial separation of NR and R was seen following hierarchical clustering and PCA, with R vs. NR being identified as a significant source of variation. Regardless of the specific low-level analysis approach, the classifiers performed similarly (conserved estimate of accuracy: 64% PLIER, 65% RMA). Certain biological pathways were associated with those classifiers who were selected in high frequency in the tested classification models (table). The majority of transcripts associated with these pathways were common to both low-level analysis methods, while additional unique transcripts for each pathway were identified with each method. Conclusions: (i) Gene expression profiling of CD34+ cells selected from cryopreserved bone marrow is feasible. (ii) This pilot study suggests that transcript profiling may have clinically useful predictive value in identifying patients unlikely to respond to imatinib. A larger study is needed to determine the predictive accuracy and obtain a clear understanding of the clinical relevance of those values. (iii) The classifiers are dominated by transcripts associated with PI3K and MAPK signalling, cell cycle, cell adhesion and nucleic acid metabolism. Pathway RMA Unique Transcripts PLIER Unique Transcripts Shared Transcripts Cell Cycle 4 1 14 Complement & Coagulation Cascades 8 2 9 Oxidative Phosphorylation 8 3 4 Phosphatidylinositol Signaling System 8 1 15 Purine Metabolism 9 2 13 Pyrimidine Metabolism 9 2 9

T-cell prolymphocytic leukemia (T-PLL) is rare and presents with widespread disease. Indolent presentations are seen but eventually progress. The disease shows marked chemoresistance and is best treated with the monoclonal anti-CD52 antibody (CAMPATH). Prolymphocytes show a post-thymic phenotype and are CD4+CD8− (65%), CD4−CD8+ (10%) or CD4+CD8+ (25%). This double positive phenotype, raises questions about the putative ontology of T-PLL. Morphological heterogeneity, with typical (75%), small cell (20%) and cerebriform/sezary-like variants (5%) is described. Inversions or reciprocal translocations of chromosome 14 involving breakpoints at q11 (TCR a/d) and q32.1 (TCL1 and TCL1b) are seen (~ 80%). Other common abnormalities involve chromosome 8, translocation (X;14)(q28;q11) and, ATM (11q23). We investigated the clinico-pathological heterogeneity in T-PLL, at the level of the transcriptome and evaluated the ability of gene expression profiling to sub-classify T-PLL. Total RNA was extracted from blood prolymphocytes (>92% purity) of 22 patients. cDNA synthesis followed by biotin-labelled cRNA synthesis was carried out as per Affymetrix protocols. Fragmented cRNA was hybridized to the Human U133 PLUS2 GeneChip array (54K probes). Microarray services were provided by MRC geneservice (UK HGMP Resource Centre). Hierarchical clustering of samples was performed using a filtered gene set (12,456) and >4 different algorithims. Prediction analysis for micoarray (PAM) and significance analysis of microarray (SAM) were used to evaluate class performance, and partition genes using pre-defined labels of immunophenotype, karyotype, response and morphology. Validation was performed by RT-PCR in a subset of genes.Unsupervised analysis robustly and reproducibly partitioned samples into 2 groups; A (n=8) and B (n=14). SAM analysis identified 4487 differentially expressed transcripts (false discovery rates 40% of which showed >2-fold difference in expression between the groups. There was no statistical difference in age, immunophenotype or karyotype betweeen groups, however, differential response to CAMPATH was seen. PAM analysis refined a sub-group of ~123 genes which most efficiently differentiated these groups. Group A showed significantly higher rates of non-response and progressive disease as compared to group B (n=14, p=0.036). Key differences related to apoptosis and cell-cycle associated gene expression. Down regulation of caspases (CASP1, CASP2,CASP4, CARD8 and CASP8AP2), cyclins (CCNC, CCND2, CCND3, CCNG1, CCNI, CCNT2), bcl-2, HDAC1, HIPK2, IL6R and ATM were frequent in group A with upregulation of genes implicated in NF-kB (TRAF4, SQSTM1) and TNF pathways (LMNA, ARTS-1), as well as transcription factors such as ATF-3. CD52 expression was ~2-fold higher in group B and may explain in part, differential responses to CAMPATH. RT-PCR validated gene expression data for LMNA and ATF-3. Despite the small numbers, algorithim-independent segregation into 2 consistent groups, in conjunction with the magnitude of gene differences, presence of many mutually exclusive divisions, and low prediciton errors, imply that the 2 identified profiles arise from fundamental differences at a regulatory level and thus likely represent a generalisable classification for T-PLL. Differential responses to CAMPATH may be a sub-feature of this grouping.

B-cell prolymphocytic leukemia (B-PLL) is a rare disease, originally described in the early seventies and now recognized as a specific entity by the WHO classification. Diagnosis is based on clinical features and lymphocyte morphology (>55% circulating prolymphocytes), as no specific immunophenotypic or cytogenetic marker is available. According to WHO, cases of chronic lymphocytic leukemia (CLL) with increased prolymphocytes (CLL/PL) (>10% and 4 different algorithms. Prediction analysis for microarray (PAM) and significance analysis of microarray data (SAM) were used to evaluate class performance, and to partition genes using a priori defined labels of morphology, immunophenotype and cytogenetics. Unsupervised analysis reproducibly partitioned samples into two homogeneous distinct groups, corresponding to the diagnoses of B-PLL and CLL. SAM analysis identified 3957 differentially expressed transcripts (false discovery rates 77% of which showed an over 2-fold difference in expression between the groups. PAM analysis refined a sub-group of 46 genes which most efficiently differentiated the two diseases. Differentially expressed genes included those encoding surface antigens, oncogenes, transcription factors, adhesion molecules or involved in cell cycle/cell proliferation, lipidic metabolism and catalytic protein activity. Comparison of CD5 positive (13) versus CD5 negative (7) cases and cases with (7) or without (13) del(p53) showed no reliable class prediction. Our study formally demonstrates that B-PLL and CLL are two distinct diseases, each showing a specific gene expression. B-PLL has a homogeneous genomic profile irrespective of its heterogeneity in laboratory features. Validation of a model based on the expression of few genes predictive of diagnosis is on going. Further analysis of these data may also identify specific genes involved in B-PLL pathogenesis and drug resistance.

Risk stratification remains limited for patients being treated for T-ALL due to a lack of biologic predictors of outcome. As a consequence, treatment assignment on modern protocols has been largely achieved through random assignment. Recent observations have suggested that overexpression of specific gene(s) may provide a reliable means to risk stratify patients. We hypothesized that microarray analysis may identify gene sets that distinguish both therapeutic response and patient outcome in T-ALL. We analyzed the gene expression profiles of 45 primary T-ALL samples (24 CCR, 21 relapse) from a matched, case control study with sufficient cRNA for microarray analysis (COG #8704). We performed oligonucleotide microarray analysis using Affymetrix U133Av.2 genechips which have approximately 54,000 target genes and ESTs. Following heirarchical clustering in dChip and R Language analyses (Chiaretti et al. Blood, 2004), but using RMA normalization, we identified 37 genes that serve as reliable predictors of CCR or relapse. Leave-one-out least discriminant analysis cross-over validation further constrained our prognostic gene identifiers to 21 genes of robust significance. These 21 genes predict 87 % of CCR and 82% of relapse accurately (p

Myeloid Metaplasia with Myelofibrosis (MMM) is a myeloproliferative disorder (MPD) associating ineffective and extramedullary hematopoiesis with progressive splenomegaly, bone marrow fibrosis and neoangiogenesis. The myeloproliferation is characterized by an increased number of circulating CD34+ cells with the prominent amplification of dystrophic megakaryocytes (Mk). We compared the transcriptome of CD34+ and Mk cell from the peripheral blood (PB) of MMM patients and from the PB and bone-marrow (BM) of unmobilized healthy donors. Application of multivariate analyses (principal component analysis and classification by partitioning around medoids algorithm) on Gene Ontology annotation of differential genes allowed a global functionally approach of the main cellular dysregulated pathways in MMM. Each sample cRNA probe was individually and differentially hybridized to the cRNA reference probe on a Human Oligo-microarray 22K (Agilent) and data were normalized by application of the local LOWESS algorithm. Comparison of the CD34+ cell transcriptome between MMM and healthy donors revealed a global down regulation of 2/3 of the expressed genes in contrast to 1/3 of genes that are up-regulated after data filtration by significance analysis microarray (SAM) algorithm (threshold p

Polycythemia vera (PV) is a myeloproliferative disease characterized by accumulation of erythrocytes and cells of the myeloid and megakaryocyte lineages. Although genes like PRV-1 and PTP-MEG2 have been implicated in the pathology of PV, there is no consensus on their importance in the disease process. Progenitor cells from PV patients can grow in the absence of erythropoietin, and are hypersensitive to a variety of other growth factors. This suggests that polycythemic hematopoietic progenitor cells possess a significantly different genetic program. We tested this idea by molecular profiling hematopoietic progenitor cells (CD34+) from PV specimens and normal donors. We purified CD34+ cells from the marrow of 10 PV patients and harvested total RNA. Biotinylated cRNA was made through two rounds of linear amplification, and hybridized to Affymetrix HGU133A genechips. CD34+ cells from marrow mononuclear cells of 5 normal controls were processed similarly. The resulting datasets were normalized to the median across chips and across genes. Unsupervised hierarchical clustering showed that PV samples had a distinct gene expression profile from the controls. We then performed supervised clustering using a non-parametric t-test (Wilcoxon rank sum test) using the Benjamini and Hochberg multiple testing correction held to a p-value of 0.01 to determine genes that were significantly different between disease and normal samples. Using these stringent criteria, there were 331 genes that reached significance. Strikingly most of these were decreased in expression compared with control CD34+ cells and only 34 genes were upregulated in PV. A 35 gene predictor set was discovered through the use of a k-nearest neighbor metric. This set was 100% accurate for the prediction of PV in a leave one out cross-validation approach. Among these genes are EVI1, a known oncogene and one of only two genes upregulated in PV on this list, and the putative tumor suppressor genes TUSC4 (NPR2), NDRG1 and KLF4. Also among the predictor genes is BAALC, a gene expressed in normal CD34+ cells and known to be a prognostic indicator gene for acute myeloid leukemia.

Acquired and congenital defects in iron metabolism from either deficiency or excess are one of the most common human diseases. Here we present the characterization of the zebrafish frascati mutation, which results in a profound hypochromic anemia and a developmental arrest at the pro-erythroblast stage. Using a positional cloning strategy, we have identified the gene disrupted frascati in mutants as a novel member of the mitochondrial solute transporter family. Members of this family of solute carriers have related tripartite sequence and structure. They function in transporting various metabolites and substrates across the inner mitochondrial membrane. We have verified the identity of the gene in zebrafish by the following criteria: (a) tissue-restricted expression in erythroid progenitors, (b) identification of missense mutations from the frascati five alleles, (c) rescue of anemia by over-expression frascati of cRNA in mutant embryos, and (d) mimicry of anemia using inactivating antisense morpholinos in wildtype embryos. We have also identified the functional ortholog in the mouse which has an analogous tissue and developmental expression pattern. The frascati ortholog in the mouse is highly expressed in fetal liver and adult bone marrow and spleen. The murine frascati transcript and protein are induced during terminal erythroid differentiation in MEL cells treated with either DMSO or HMBA. The over-expression of the mouse frascati cRNA in zebrafish frascati mutant embryos rescued their anemia with equal efficacy as the zebrafish clone. Given the identity of the gene and the requirement for iron in heme biosynthesis in the mitochondria of the developing erythron, we injected exogenous iron-dextran into frascati embryos. The embryos injected with iron-dextran were allowed to develop to 3 days post-fertilization, then stained for hemoglobinized cells with o-dianisidine and genotyped. Using this assay, the anemia caused by frascati the mutation could be partially rescued with exogenous iron supplementation. We therefore propose that the frascati gene functions as the essential transporter for iron importation into the mitochondria for heme biosynthesis and subsequent hemoglobin production in developing erythroid progenitors. Insight into the function of frascati the gene will be directly relevant to our understanding of human disorders of iron deficiency anemia and iron-overload sideroblastic anemia.

FA stem cells and progenitor cells are apoptotic in the ground state and hypersensitive to a variety of extracellular apoptotic cues. The relative lifetime risk of AML or MDS in FA is high and clonal cytogenetic defects are universally found in evolved clones, but the mechanisms involved in leukemogenesis are unknown. The combined influences of genetic instability and high level stem cell apoptosis represents a de-facto selective pressure that in our view favors the emergence of more resistant stem cell clones. Predictions of this model are: (1) Non clonal FA progenitor cells (FAwt) are hypersensitive to apoptotic cues but FA progenitors derived from cytogenetically abnormal clones (FAclon) will be resistant, (2) anti-apoptotic events that attend cytogenetic clonal evolution should interdict precisely those apoptotic pathways activated by FA gene mutations, and (3) Key transcripts differentially expressed in the normal (N) vs. FAwt comparison should normalize (ie. should register as "no change") in the N vs. FAclon comparison. Preliminary studies support the first 2 predictions (Lensch et al, Leukemia, 1999 and Haneline et al, Blood, 2003). To further test the adaptive model, we compared transcriptomes of 41 low density bone marrow cell samples: (11 normal volunteers [N], 9 FAclon [marrow replaced [>75%] by cells bearing clonal cytogenetic defects] and 21 FAwt [no cytogenetic abnormalities]). RNA was prepared in the participating institutions and shipped to Portland on dry ice. Complementary DNA and cRNA was prepared, cRNA fragments labeled for use as targets of the probes in the human U133A Affymetrix chip. MAS 5.0 was utilized to process images, quantify signals, adjust background, and to scale the data. A linear mixed model was used for inter-chip normalization. Unsupervised hierarchical clustering and multidimensional scaling (MDS) distinguished N from FAwt samples but FAclon fell within the mathematical space defined by the N RNA. Of 17,044 genes tested, 1,430 were expressed differentially (FDR adjusted p

Cryopreservation of patient bone marrow specimens allows for future purification of tumor cells for research or diagnostic purposes. It has not been determined whether this practice causes significant changes in gene expression. In order to evaluate this, we performed microarray analysis (Affymetrix U133A and U133A 2.0 gene chips) on mononuclear cells from 5 acute myelogenous leukemia (AML) patient samples (>70% blast count) that had been either a) cryopreserved, b) snap frozen, or c) resuspended in TRIzol and stored at −80°C. Samples remained frozen for a period of 1–4 weeks. While gene expression changes between groups were minor, there were some differences. Thirteen probe sets between TRIzol and cryopreserved samples, and 60 probe sets between TRIzol and snap frozen samples, varied significantly (>2-fold difference and p

To better understand the cellular events that precede onset of clinically significant acute GVHD, a complication of allogeneic HSCT, we compared global gene expression profiles in patients 3 (days 18–22) and 4 (days 28–32) weeks after transplant. Patients in this study underwent myeloablative-conditioning regimen prior to receiving a T cell replete PBSCT from a related (n=9) or unrelated donor (5 HLA matched and 4 mismatched). Blood was obtained prospectively at scheduled times (prior to administration of glucocorticosteroids). RNA was isolated from nucleated blood cells and biotin-labeled cRNA hybridized on Affymetrix HG-U133A chips. MAS 5.0 software was used to extract gene expression values. We initially compared gene expression profiles between 15 patients 3 weeks post-HSCT and 10 normal controls. A total of 1176 genes were differentially expressed with statistical criterion of NFD (number of false discovery) equal to 10. Gene profiles for these 1176 genes were compared between 8 patients who subsequently developed GVHD within 1–5 days and 7 patients who remained GVHD free for 90 days. A limited number of genes were differentially expressed with NFD=1: 3 genes in GVHD patients showed increased expression and 6 showed decreased expression. A second set of experiments was performed to compare changes occurring within individual patients over an interval of 7 days (between weeks 3 and 4) prior to diagnosis of clinically significant GVHD (onset between days 27–32). We used a pair-wise comparison with selection criterion NFD=1. Increased expression prior to GVHD was observed in 55 genes and decreased expression in 88 genes. Approximately 50 of these genes were associated with inflammation and cellular stress response. Using the same statistical criterion we compared gene profiles between weeks 3 and 4 for 3 patients who remained GVHD-free for at least 90 days. Fewer changes were observed with increased expression occurring in 6 genes and decreased expression in 14 genes. These differentially expressed genes did not overlap with the candidate genes associated with the development of GVHD. Genes showing expression changes in GVHD included: Increased Decreased Inflamamtory Response IFN-α10, IL8, IL17 Transcription Factors NFATC1 GATA3 Cell Surface/Signal Transduction CD6, CD7, CD8, TCR-interacting molecule, MAP4K1, TNFRSF25, Effectors Molecules GRMM AICD/Apoptosis TOSO, BAX Cellular Stress Response DDAH1 DLAT, PKC1, COX5B These results suggest that extensive complex gene expression changes occur among nucleated blood cells during the early post-transplant period presumably due to extensive alterations in cellular activation occurring during reconstitution. The preliminary results of the longitudinal analysis of changes occurring within individual patients indicate that early post-transplant studies are feasible and that they may be informative for yielding insight into the molecular events associated with development of clinically significant GVHD. These data also indicate a paradoxical decrease in certain T cell associated genes in GVHD. However alloimmune induced T cell activation may lead to AICD and previous studies have demonstrated increased apoptosis among peripheral blood T cells in GVHD patients. Further studies including gene expression profiling of isolated T cells will be necessary to determine if this approach can be useful in identifying a molecular “signature” for GVHD that may be useful for diagnosis and monitoring.

We tested the feasibility of performing gene expression profiling using amplified RNA from Fine Needle Aspiration (FNA) obtained from lymph nodes. Twenty-four samples from patients with a diagnosis of Follicular Lymphoma (FL) or Large B-Cell Lymphoma (LBCL) were obtained after informed consent. The diagnosis were performed by two pathologists and classified into two groups (10 LBCL and 14 FL) using conventional morphology and immunophenotyping. Total RNA was extracted using RNaqueous (Ambion, Austin, TX). Total RNA (100ng /per sample) was subjected to 2 cycles of standard double-stranded cDNA synthesis using Superscript Choice System (Invitrogen, Grand Island, NY) and in vitro transcription for target amplification as per GeneChip’s Eukaryotic Small Sample Target Labeling protocol (Affymetrix, Inc, Santa Clara, CA). The biotinylated cRNA from each sample was hybridized to Affymetrix HG-U133A chips. Gene expression profiling results were first analyzed by Principal Component Analysis (PCA) using a list of 146 probe sets representing 62 genes characteristic of Acivated B-Cell (ABC) or Germinal Center (GC) signature. This analysis identified 5 LBCL cases with ABC cell signature (Fig 1). Using a list of 207 probe sets representing 113 genes involved in FL transformation (K Elenitoba-Johnson, PNAS 2003), PCA analysis identified two overlapping clusters corresponding to FL and GC-DLBCL. To further improve this classification, we generated a list of 82 genes differentially expressed between FL and GC-LBCL. Using this list of genes, PCA analysis demonstrated a clear separation between FL and GC-LBCL (Fig 2). These results demonstrate that comprehensive transcription profiles can be performed in patients with NHL using RNA obtained from FNA and that one can distinguish among FL and LBCL lymphoma genetic subtypes. Figure Figure