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BACKGROUND: Chronic myeloid leukemia (CML) is a clonal myeloproliferative disorder with heterogeneous biological and clinical features. The biomolecular mechanisms of CML response to tyrosine-kinase inhibitors are not fully defined. OBJECTIVE: We undertook a gene expression profiling (GEP) study of selected bone marrow (BM) CD34+/lin-cells of chronic-phase CML patients at diagnosis and after 12 months of TKI nilotinib to investigate molecular signatures characterizing both conditions. METHODS:We selected and counted BM CD34+/lin- cells of 30 CML patients at diagnosis and during 3, 6 and 12 months of first-line nilotinib treatment. GEP was performed between CD34+/lin- cells of patients at diagnosis and the same patients after 12 months of nilotinib. RESULTS: The number of BM CD34+/lin- cells dramatically decreased after 3, 6 and 12 months of nilotinib. GEP detected 264 statistically significant differentially expressed genes at diagnosis vs. 12 months of nilotinib. Functional enrichment analysis revealed groups of genes belonging to 14 pathways differentially active during nilotinib treatment. CONCLUSIONS: In conclusion, lipid, glucose and sphingolipid metabolism, insulin resistance, complement and coagulation, platelet activation, cytoscheleton, cell adhesion, transport, B cell differentiation, RAS-signaling pathway, proliferation, growth factors, and apoptosis were significantly deregulated between CML patients at diagnosis and after 12 months of nilotinib.

Chronic myeloid leukemia (CML) is characterized by the constitutive tyrosine kinase activity of the oncoprotein BCR-ABL1 in myeloid progenitor cells that activates multiple signal transduction pathways leading to the leukemic phenotype. The tyrosine-kinase inhibitor (TKI) nilotinib inhibits the tyrosine kinase activity of BCR-ABL1 in CML patients. Despite the success of nilotinib treatment in patients with chronic-phase (CP) CML, a population of Philadelphia-positive (Ph+) quiescent stem cells escapes the drug activity and can lead to drug resistance. The molecular mechanism by which these quiescent cells remain insensitive is poorly understood. The aim of this study was to compare the gene expression profiling (GEP) of bone marrow (BM) CD34+/lin- cells from CP-CML patients at diagnosis and after 12 months of nilotinib treatment by microarray, in order to identify gene expression changes and the dysregulation of pathways due to nilotinib action. We selected BM CD34+/lin- cells from 78 CP-CML patients at diagnosis and after 12 months of first-line nilotinib therapy and microarray analysis was performed. GEP bioinformatic analyses identified 2,959 differently expressed probes and functional clustering determined some significantly enriched pathways between diagnosis and 12 months of nilotinib treatment. Among these pathways, we observed the under expression of 26 genes encoding proteins belonging to the cell cycle after 12 months of nilotinib treatment which led to the up-regulation of chromosome replication, cell proliferation, DNA replication, and DNA damage checkpoint at diagnosis. We demonstrated the under expression of the ATP-binding cassette (ABC) transporters ABCC4, ABCC5, and ABCD3 encoding proteins which pumped drugs out of the cells after 12 months of nilotinib. Moreover, GEP data demonstrated the deregulation of genes involved in the JAK-STAT signaling pathway. The down-regulation of JAK2, IL7, STAM, PIK3CA, PTPN11, RAF1, and SOS1 key genes after 12 months of nilotinib could demonstrate the up-regulation of cell cycle, proliferation and differentiation via MAPK and PI3K-AKT signaling pathways at diagnosis.

In this study we focused on determining the transcriptome differences between Waldenström's Macroglobulinemia (WM) and IgM Monoclonal Gammopathy of Undetermined Significance (IgM-MGUS) by gene expression profiling (GEP) considering all the different transcript isoforms of genes that map the human transcriptome (coding transcripts, non-coding transcripts). We performed the analysis on BM B cell clones (CD45+,CD38+,CD19+,LAIR-1-,CD27dim,IgM+,CD22dim,CD25+) from WM (n=21) and IgM-MGUS (n=13) patients. These populations were identified with an 8-colors panel by flow cytometry and successively isolated by cell sorting. GEP of WM vs. IgM-MGUS BM B cell clones was performed using Affymetrix Gene Chip HTA 2.0. Data was first pre-processed using Affymetrix Expression Console and then normalized using ComBat (Johnson et. al. Biostatistics2007) and quantile normalization. We investigated both differential expression using SAM test (Tusher et. al. Proc Natl Acad Sci USA2001) and differential variability using F-test to compare means and variances between groups, respectively. In particular, testing the variability is useful to investigate a heterogeneous disease like Waldenström's Macroglobulinemia as well as IgM-MGUS, since B clonal cells proliferation and growth are driven by different mutations acting as perturbations in different molecular pathways, and these perturbations vary from individual to individual. False Discovery Rate (FDR) p-values adjusted for multiple testing below 5% were considered significant (Storey J R Stat Soc 2002). "Genomic Regions Enrichment of Annotations Tool" (GREAT) (McLean t. al. Nat Biotechnol 2010) was used to annotate the selected probe sets and perform biological pathway enrichment analysis. We considered 67,529 probe sets for the analyses. There were no differentially expressed probe sets in means after the correction for multiple comparisons, whereas 446 probe sets showed differential variability between IgM-MGUS and WM samples. Figure 1 shows how the selected probe sets map on the human genome according to GREAT. Enrichment analysis performed on these 446 probe sets showed after correction for multiple testing (FDR threshold set at 5%), significant enrichment for apoptosis, B cell receptor signaling pathway, chemokine-signaling pathway, ERBB signaling pathway, PI3K-AKT signaling pathway and WNT signaling pathway. Of note, BCL2, RAF1, MAPK1, GRB2, GSK3B, NRG1, SOS1, WNT5A, NLK, PTK2B genes belonging to these pathways, demonstrated significant different expression variability. We found that IgM-MGUS showed significantly increased variability of expression of all the selected genes (a part from SOS1 and NLK) across patients. We could speculate that IgM-MGUS B cell clones showed increased expression variability in the identified genes in their developmental stage, indicating the likely presence of cells at different step of differentiation whereas the expression of the same genes was more stable in WM patients. In summary, we found that IgM-MGUS was characterized by higher variability in gene expression across patients, which could be related to higher intra-patient variability suggesting the possible link between expression variability and genetic heterogeneity. Important functions showing increased variability in IgM-MGUS compared to WM were related to apoptosis, B cell receptor signaling pathway, chemokine-signaling pathway, ERBB signaling pathway, PI3K-AKT signaling pathway and WNT signaling pathway. Larger datasets and clinical evolution of IgM-MGUS individuals would provide a deeper insight into the functional context of the pathways and the differential variable genes highlighted by the comparison between IgM-MGUS and WM. Figure 1 Distribution of the 446 probes showing significant differential variability in IgM-MGUS vs. WM samples. Figure 1. Distribution of the 446 probes showing significant differential variability in IgM-MGUS vs. WM samples. Disclosures No relevant conflicts of interest to declare.

Several studies demonstrated IGVH mutational status and ZAP70 expression as the most relevant prognostic markers in Chronic Lymphocytic Leukemia (CLL), suggesting the separation of two patient subgroups: with good mutated ZAP70 negative (MTZAP70(-) and poor unmutated ZAP70 positive (UMZAP70(+)) prognosis.We determined the gene expression of B cells in 112 CLL patients divided into three classes: class 1 with MTZAP70(-), class 2 with UMZAP70(+), and class 3 included both UMZAP70(-) and MTZAP70(+).We found LPL, AGPAT2, MBOAT1, CHPT1, AGPAT4, PLD1 genes encoding enzymes involved in lipid metabolism overexpressed in UMZAP70(+). In addition, this study identified ARSD, a gene belonging to the sphingolipid metabolism, as a new gene significantly overexpressed in UMZAP70(+) compared to MTZAP70(-). Western blots confirmed that ARSD protein levels were significantly different between the 3 classes of patients and normal controls. Statistical analysis identified a significant correlation between ARSD and IGVH; however, both ARSD protein level and IGVH were independently associated with the need for therapy of CLL patients.ARSD is a novel prognostic factor as the time to start therapy is shorter in patients with high levels of ARSD protein and sphingolipid metabolism could represent a new biological mechanism in CLL.

The study analyzed 30 chronic phase CML patients at diagnosis and at 3, 6 and 12 months of first-line nilotinib treatment. As optimal molecular response was achieved at 3, 6 and 12 months after nilotinib in all the 30 patients (figure 1), we established cut off values of molecular response (MR) to define groups of CML patients (n =30), and to investigate differences of gene expression profiles between patients at diagnosis based on the MR achieved after 3, 6 and 12 months of nilotinib, respectively. We used the following cut off values: 1% IS at 3 months of nilotinib, 0.1% IS at 6 months of nilotinib, and 0.01% IS at 12 months of nilotinib. Patients were divided into 2 groups based on MR values of each patient at 3 months of nilotinib: group A (n =24) with MR ≤1.0% IS and group B (n =6) with MR >1% IS. Based on the values of MR at 6 months of nilotinib, patients were divided into 2 groups: group C (n =22) with MR ≤0.1% IS and group D (n =8) with MR >0.1% IS. At 12 months of nilotinib, patients were divided into the following groups: group E (n =18) with MR ≤0.01% IS and group F (n =12) with MR >0.01% IS. Gene expression profiling (GEP) on the selected bone marrow (BM) CD34+/lin- cells of 30 CML patients at diagnosis was performed using Affymetrix GeneChip HTA 2.0. GEP data was preprocesses using RMA. SAMR and GSEA were used to detect differentially expressed genes and pathways (i.e. MSigDB Canonical pathways and GOBP gene sets) associated with the different groups, respectively. In both cases, correction for multiple testing was performed using the false discovery rate procedure with a threshold 0.05 for SAMR and 0.25 for GSEA as suggested by the software developers. GSEA detected significant differences in the transcriptional signature between group A and group B associated with the MR at 3 months as well as between group C and group D in respect to the MR at 6 months of nilotinib, whereas no genes were identified as significantly differentially expressed. Based on the MR at 3 months, we identified Reactome pathways "P130 cascade" and "GRB2 SOS linkage to MAPK signaling for integrins" significantly upregulated in group A compared to group B. FGA, FGB, FGG, APBB1IP, ITGA2B and CRK genes contributed to call the pathways upregulated. In vitro studies (Ding J et al. PloS One, 2013) demonstrated that nilotinib induced dephosphorilation of the BCR-ABL1 target CRK oncogene which acts in the CML hematopoietic stem cells like an adaptor in diverse signaling pathways and cellular processes playing an apoptotic role in CML. Our GEP results highlighted that the Reactome pathway involved in MAPK signaling (CRK gene) wasover expressed in CML patients with a better MR ≤1% IS at 3 months of nilotinib. Pathway "aminoacid and amine transport across the plasma membrane" was also significantly over expressed, whereas "lipid metabolism" was significantly down regulated (genes AKR1C1 and ANGPTL3) in group A compared to group B, respectively. Based on the MR at 6 months, GEP results showed that "positive regulation of cytokine secretion" and "interleukin 1 secretion" were significantly upregulated in group C compared to group D, involving PYCARD8, NLRP2, NLRC4, NLRP3 and CARD8 genes. Of note, PYCARD and CARD8 represent key mediators, which promote caspase-mediated apoptosis processes involving the recruitment of certain recognition receptors, such as NLRP2, NLRP3 and NLRC4. In addition, CARD8 and PYCARD promote apoptosis inhibiting NFKB activation. In conclusion, the study showed that MAPK signaling for integrins and amine transport across the plasma membrane were significantly over expressed in CML patients with a MR ≤1.0% IS while lipid metabolism was significantly over expressed in CML patients with a MR >1% IS after 3 months of nilotinib. After 6 months of nilotinib, positive regulation of cytokine secretion and interleukin 1 secretion were significantly over expressed in CML patients with a MR ≤0.1 IS compared to patients with a MR >0.1% IS. We identified distinct sets of genes involved in apoptotic processes differentially expressed in 30 CML patients based on the MR at 3 months as well as 6 months of first-line nilotinib treatment. Studies in a larger cohort of CML patients are ongoing to define the expression, role and functions of the genes regulating apoptosis CRK, PYCARD and CARD8 as candidate prognostic factors for molecular response to first-line nilotinib treatment. Disclosures No relevant conflicts of interest to declare.

Waldenstrom macroglobulinemia (WM) (symptomatic and indolent) and immunoglobulin M (IgM) monoclonal gammopathy of undetermined significance (IgMMGUS) can be identified based on the bone marrow (BM) infiltration and the existence of symptoms. The purpose of this study was to investigate the biological and genetic characteristics of both disorders comparing the molecular signature of WM versus IgMMGUS using microarray analysis. We investigated BM CD19 + cells isolated from 21 WM patients and 10 IgMMGUS cases, and CD138 + BM cells isolated from all of the WM patients and 4 of the IgMMGUS cases. Gene expression profiling of WM versus IgMMGUS CD19 + cells highlighted 151 differently expressed genes and the comparison with CD138 + cells demonstrated 43 differently expressed genes in WM versus IgMMGUS. Regulation of transcription, Janus kinase/signal transducer and activator of transcription, PI3K/Akt/mammalian target of rapamycin, mitogen-activated protein kinase signaling pathways are the relevant gene ontology biological processes occurring in CD19 + cells, and immune response, cell activation, and signaling processes developing in CD138 + cells mainly distinguish WM and IgMMGUS.

This study aimed to determine the gene expression profiling (GEP) of bone marrow (BM) CD34+/lin- cells of CML patients at diagnosis and after nilotinib treatment.Microarray was performed on 30 CML patients at diagnosis as well as 8 patients after 12 months of nilotinib treatment using the latest generation Affymetrix GeneChip HTA 2.0 to further investigate genomic complexity. Bone marrow blood (in a range of 5-20 ml) samples were collected from patients at diagnosis and after 3, 6 and 12 months of nilotinib treatment. BM mononuclear cells were purified by density gradient centrifugation at 800 rpm for 20 min and soon after CD34+/lin- cells were isolated using Diamond CD34 Isolation Kit (Miltenyi Biotec). The purity of CD34+/lin-cells was about 97% as determined by flow citometry and the cells were counted at diagnosis and after the treatment with nilotinib (at 3, 6, and 12 months). BM CD34+/lin- cells of 30 patients at diagnosis and after 3 and 6 months of nilotinib were resuspended in RNAlater (Ambion, Life Technologies) and stored at -20°C until RNA extraction was performed. Total RNA was extracted from BM CD34+/lin- cells using MagMAX 96 Total RNA Isolation Kit (Ambion). BM CD34+/lin- cells were counted and a range of 100,000-800,000 has been noted at diagnosis. cRNA was prepared according to OvationPico WTA System V2 kit followed by Encore Biotin Module Kit (NuGEN). Ultimately, cRNA was hybridized to HTA 2.0 and signals were scanned by Affymetrix GeneChip Scanner 3000 following the manufacturer’s instructions. We observed that the number of CD34+/lin- cells dramatically decreased at 3 months (1,000-600,000), after 6 months (1,000-260,000) and after 12 months (100-130,000). In particular, CD34+/lin- cells were even less than 1000 after 12 months of nilotinib. If the number of CD34+/lin- cells after 12 months of nilotinib was less than 1000, we directly resuspended the cells in Prelude direct Lysis module (NuGEN) and microarray experiments were performed without RNA extraction. In this case, cRNA was prepared using Ovation One Direct System kit followed by Encore Biotin Module Kit (Nugen) and finally hybridized to the HTA 2.0. Microarray experiments were performed on CD34+/lin- cells of 30 CML patients at diagnosis with HTA 2.0. Moreover, we performed microarray experiments on CD34+/lin- cells of 8 CML patients at diagnosis vs. 8 CML patients after 12 months of nilotinib using HTA 2.0. Data was preprocessed and normalized using Robust Multi-array Average (RMA) algorithm. The Significant Analysis of Microarrays (SAM) was used to identify genes with statistically significant changes in expression in CML patients at diagnosis and at 12 months of treatment. P-values were corrected for multiple testing using false discovery rate. In conclusion, as far as we know, this is the first study which has isolated BM CD34+/lin-cells from CML patients at diagnosis and after nilotinib treatment. We observed a wide variety of the number of CD34+/lin- cells in 30 CML patients at diagnosis as well as after 3, 6 and 12 months of nilotinib. In particular, CD34+/lin- cells of CML patients at diagnosis were over 100.000. RNA was extracted and cRNA was prepared with OvationPico WTA System V2 kit followed by Encore Biotin Module Kit (NuGEN) and finally hybridized to HTA 2.0. On the other hand, the number of CD34+/lin- cells of CML patients after 12 months of nilotinib markedly decreased as low as 100 cells. In this case, we decided to avoid RNA extraction from the cells and we directly prepared cRNA using Ovation One Direct System kit and Encore Biotin Module Kit (Nugen) followed by the hybridization to the HTA 2.0 Array. To increase the statistical power in biomarker identification, we combined samples pre-processed with different experimental protocols (NuGEN) using ComBat, an empirical Bayes framework that adjusts data for batch effects and is robust to outliers in small datasets. Principal component analysis applied to the expression data before and after ComBat adjustment shows that the adopted normalization procedure effectively removes the systematic bias introduced by different protocols. Disclosures No relevant conflicts of interest to declare.

Background CML is a clonal disorder characterized by the presence of the Philadelphia (Ph) chromosome which encodes for the bcr-abl tyrosine-kinase (TK). Target therapy with the TK inhibitors (TKIs)) has greatly improved its outcome. Treatment with second generation TKIs - e.g. nilotinib (NIL) - results in deeper and faster responses and prevents disease progression. Sustained responses may enable TKI discontinuation. However, even in the event of qPCR negativity, a fraction of patients (pts) experience disease recurrence possibly due to persistence of quiescent leukemic stem cells (LSCs). Degree and mechanisms of LSCs clearance during TKI treatment are not established yet and conflicting results are reported in the literature. Work from the group of Bocchia (Bocchia 2008; Defina 2012) showed reduction of LSCs during long term imatinib (IM) therapy; moreover, in CCyR pts residual LSCs are more rarely detected after NIL compared to IM therapy and, in a small fraction of pts this occurs after very short-term NIL therapy. This data conflicts with in vitro evidence that NIL is not superior to IM in inducing growth suppression in CML LSCs (Konig, 2008). To verify the in vivo activity and time-course of first-line NIL therapy on bone marrow (BM) Ph+ stem cells (CD34+/lin-) clearance, on behalf of the Rete Ematologica Lombarda (REL) the PhilosoPhi34 study (EudraCT: 2012-005062-34) was designed. Primary efficacy endpoint was to measure the rate of BM residual CD34+/lin-Ph+ cells in CCyR pts at 6 months of treatment. Methods BM cells were collected and stored at diagnosis and at 3,6 and 12 mos of treatment. CD34+/lin- cells were purified using a Diamond CD34 Isolation Kit Miltenyi (97% of purity). FISH analysis of selected unstimulated CD34+/lin- cells was performed according to standard procedures; considering the low sensitivity of the test, in order to define the test as negative at least 200 nuclei were examined. The A'Hern single stage design was chosen for the present study; considering the CCyR results obtained in the ENESTnd study and the anticipated number of un-evaluable tests, a minimun of 87 pts were required. Results Enrolment of the 87 pts was completed by June 2015. Table 1 summarises pts' characteristics and response to treatment. FISH results are as follows: at 3 mos, 8/65 (12,3%; CI 95%: 2,3%-15,7%) evaluable FISH tested positive (10 negative tests not evaluable); at 6 mos 5/71 (7%; CI 95% :2,3-15,7%) evaluable FISH tested positive (7 negative tests not evaluable); at 12 mos, 0/68 (0%; CI95%:0,0-5,2%) evaluable FISH tested positive (9 negative tests not evaluable). At any time point, Sokal score did not predict for FISH results. However, as outlined in Table 2, H-Sokal score pts are less prevalent among pts who achieve a CCyR, a requirement for FISH analysis. Of the 4 pts who failed the treatments' objectives by 12 mos, 1 was in CCyR with detectable residual CD34+/lin-Ph+ cells at 3 mos; 2 were not in CCyR and with residual CD34+/lin-Ph+ cells at 3mos; 1 was in CCyR and with CD34+/lin-Ph- cells at 3 and 6 mos but with increasing qPCR. Only 1 pt with CD34+/lin-Ph- cells at all time points and with optimal molecular response harboured a NIL-resistant mutation at 26 mos of treatment. None of the 22 pts (including 4 H-Sokal score pts = equal proportion of study cohort) in Molecular Response (MR) 3.0 at 3 mos had a positive FISH at 3 and 6 mos or failed treatment at follow-up. Conclusion. Our final results on the whole cohort of pts confirm our preliminary data on the efficacy of NIL 300 g BID in early clearance of BM LSCs (CD34+/lin-Ph+) in newly diagnosed CP-CML patients tested at 3, 6 and 12 mos of treatment. Moreover, according to our data, fast disease debulking seems crucial for obtaining BM LSCs clearance and it can be speculated that the same mechanism responsible for this early MR 3.0 achievement is also capable of preventing H-Sokal risk pts from failing treatment. Disclosures Orlandi: Ariad: Honoraria; BMS: Honoraria; Novartis: Honoraria.

BACKGROUND New prognostic factors such as IgVh mutational status, ZAP-70 protein expression and cytogenetic abnormalities have shown to offer important prognostic information for patients with chronic lymphocytic leukemia (CLL). Our aim was to evaluate the optimal cut-off for IgVh mutational status, ZAP-70 expression and cytogenetic abnormalities in association with disease progression defined as the need for treatment within 3~years from diagnosis in 170 patients with B-CLL. DESIGN AND METHODS Receiver operating characteristics (ROC) analysis and multivariate general linear models (GLMs) were used to investigate the most significant cut-off values of these biomarkers and their prognostic impact. RESULTS Our findings estimated that the optimal cut-off for IgVh mutation status and for ZAP-70 protein expression was 97% and 16.5% respectively and a high concordance between the two was demonstrated. We identified 30% as being the best-cut-off for 17p-, 11q- and 6q-. In univariate analysis 17p- was found to be a significant predictor of the event only for the whole population. Multivariate analysis including all biological parameters, identified 11q deletion as the only significant regressor. CONCLUSIONS We assessed that IgVh mutational status, ZAP-70 protein and 6q- are powerful prognostic markers. Analyses of all these factors revealed that 11q deletion was the strongest predictor of disease progression in B-CLL.

The 2nd IWWM tried to define reproducible criteria for the diagnosis of IgM-MGUS and Waldenstrom's Macroglobulinemia. IgM-MGUS was defined as asymptomatic condition characterized by serum IgM monoclonal protein (MC) without morphologic evidence of bone marrow (BM) lymphoplasmacytic infiltration. The proposal of the guidelines was to classify as MGUS also patients with equivocal evidence of BM infiltration, such as those presenting clonal B-cells by multiparameter flow cytometry (MFC) in the absence of morphologic evidence of BM infiltration, as well as those with equivocal BM infiltrates not confirmed by immunophenotypic studies. Patients The diagnosis of IgM-MGUS was made in 11 patients (6 males, 5 females) according to the consensus panel criteria. The median age at diagnosis was 73 (range, 60-77). Ten patients had K light chains. The median erythrocyte sedimentation rate was 11. The MC level at diagnosis ranged from 0.1 to 1.2 g/dL (median 0.4). Only one patient had MC value > 1.0 g/dL. The median IgM value was 697 mg/dL (range 116-1790). Five of 11 IgM-MGUS patients showed a small clonal B-cell population (light-chain-isotype-positive B-cells) detected by MFC without histologic evidence of BM infiltration. Therefore, patients were divided in 2 groups: group 1 (n=5) showing a clonal B-cell population, and group 2 (n=6) with polyclonal B-cells at MFC. Methods and results We isolated BM CD19+ cells in the 11 IgM-MGUS patients using Miltenyi Microbeads and performed microarray with Affymetrix-HG-U133 Plus 2.0 array. Gene set enrichment analysis (GSEA) was performed and different sets of genes were defined based on REACTOME pathways, KEGG pathways and GO Biological Process Terms. Interestingly, 17 top-ranking gene sets including differently expressed genes, reached a nominal p-value lower than 0.001; 2 gene sets were upregulated (while 15 gene sets were downregulated in monoclonal vs. polyclonal IgM-MGUS (table 1). No genes resulted significantly differentially expressed between group 1 and group 2 using a classic SAM test for microarrays and correcting for multiple testing with a false discovery rate (FDR) threshold of 5%. Similarly, IgM and MC were not differentially expressed between the two groups, although IgM showed a nominal p-value of 0.09 (t-test). However, when using linear regression to explain each gene expression data as a function of both IgM and MC, UBTF, TRIM5, FLJ35816, RDH10 genes were selected based on a FDR equal to 5%, applied to the F-statistic p-value. In particular, the model fitting UBTF had a p-value of 9.461e-07 and an adjusted R-squared of 0.9786; table 2 displays the coefficients of the model and the related p-values, showing a positive co-regulation of UBTF with MC. Conclusions In conclusion, microarray of IgM-MGUS gives insights into gene expression differences in IgM-MGUS. Notably, UBTF is a transcription factor which plays a crucial role in the transcription of rRNA in ERK-pathway, suggesting a possible role of ERK-pathway in IgM-MGUS. Additional gene expression measurements are ongoing in a larger cohort of IgM-MGUS patients. Table 1. Upregulated gene sets in monoclonal vs. polyclonal IgM-MGUS GENE SET NAME REACTOME_XENOBIOTICS REGULATION_OF_CHROMOSOME_ORGANIZATION_AND_BIOGENESIS Downregulated gene sets in monoclonal vs. polyclonal IgM-MGUS Table 2. Linear regression results of both IgM and MC on UBTF expression GENE SET NAME REACTOME_ROLE_OF_DCC_IN_REGULATING_APOPTOSIS REACTOME_P38MAPK_EVENTS REACTOME_EARLY_PHASE_OF_HIV_LIFE_CYCLE REACTOME_MRNA_DECAY_BY_3_TO_5_EXORIBONUCLEASE KEGG_NICOTINATE_AND_NICOTINAMIDE_METABOLISM CHROMATIN_ASSEMBLY_OR_DISASSEMBLY ESTABLISHMENT_AND_OR_MAINTENANCE_OF_CHROMATIN_ARCHITECTURE PROTEIN_DNA_COMPLEX_ASSEMBLY RESPONSE_TO_DNA_DAMAGE_STIMULUS CHROMATIN_ASSEMBLY CHROMOSOME_ORGANIZATION_AND_BIOGENESIS DOUBLE_STRAND_BREAK_REPAIR CHROMATIN_REMODELING CENTROSOME_ORGANIZATION_AND_BIOGENESIS MICROTUBULE_ORGANIZING_CENTER_ORGANIZATION_AND_BIOGENESIS Table 3 Coefficient p-value Intercept 7.0738801 6.47e-12 IgM -0.0021593 3.78e-07 MC 1.1925055 0.000501 IgM:MC 0.0010577 0.000152 Disclosures No relevant conflicts of interest to declare.

We performed a transcriptome-wide analysis of bone marrow (BM) CD19+ cells of WM vs. IgM-MGUS to find the genes and key pathways mostly involved in the comparison between WM and IgM-MGUS. We isolated BM CD19+ and CD138+ cells of 36 WM and 13 IgM-MGUS using Miltenyi Microbeads and we performed expression analysis with Affymetrix GeneChip HG U133 Plus 2.0 Array. Data was processed using RMA and analyzed using SAM and a false discovery rate threshold of 5% to select the differentially expressed genes. To further select a subset of robust biomarkers, SVMs were used in a Monte Carlo bootstrap resampling schema with B=100 external training/test splits to discriminate between WM and IgM-MGUS. 641 probes were selected by SAM on CD19+ cells. SVMs permitted the selection of 66 robust biomarker genes (Fig.1) with a MCC accuracy on external samples equal to 0.87. Functional enrichment analysis demonstrated the involvement of the following pathways: - Notch signaling pathway: ADAM17 (promoting cell growth) was upregulated while AGO1 and AGO4 (negative regulation of translation) were downregulated in WM - CAPRIN1 and SORT1 (pro-apoptosis) were underexpressed while CIAPIN1 (anti-apoptosis) was overexpressed in WM - Purine/pyrimidine metabolism (cell growth): ENTPD5 (cell proliferation) was overexpressed while NT5E (catabolic process) was underexpressed in WM - Sphingolipid pathway: ACER3, COL4A3BP, GBA3 were downregulated in WM - Rho-protein signal transduction: FARP2 (cell survival) was overexpressed in WM - Transcription: ITPRIPL2, L3MBTL4 were underexpressed while NFX1, LIMS1 (cell aging) USF1 were overexpressed in WM - Immune system: HLA-C was upregulated in WM - PRKCA was underexpressed in WM and involved in 85 pathways (MAPK-NGF-EGF-VEGF-ErbB-Ras-HIF-1-mTOR-PI3K-Akt) - MIR1204///PVT1 (oncogene), METTL3 (RNA-methylation), MINA (cell survival) were upregulated in WM As a second step of the study, we investigated 7 IgM-MGUS and 11 WM (2 symptomatic and 9 asymptomatic) newly diagnosed patients. We isolated the BM B cell clones (CD45+,CD38+,CD19+,LAIR-1-; CD27dim, IgM+, CD22dim, CD25+) using cell sorting with MoFlo XDP 3 laser cell sorter (Beckman Coulter). We identified clonally restricted B lymphocyte populations using BD LSRFortessa Flow Cytometer equipped with 5 lasers. We performed gene expression profiling (GEP) of the isolated BM cell clones of WM vs. IgM-MGUS using Affymetrix next-generation GeneChip HTA 2.0 to further investigate genomic complexity. The data was preprocessed and normalized using RMA. Selection and data analysis were performed using the t-test as implemented in the Affymetrix Transcriptome Analysis Console. Functional enrichment analysis performed using DAVID, demonstrated that groups of genes belonging to relevant functional categories were deregulated in the comparison between WM vs. IgMMGUS: - CSH2, GH2, NTF3, NPY genes which belonged to the PI3K/Akt, JAK-STAT, MAPK pathways - CSHL1 regulating cell growth - CLDN19 involved in the cell adhesion - EIF2AK3, EIF2B4 were involved in the GSK3 signaling (cellular proliferation, migration, inflammation, immune responses, glucose regulation and apoptosis) - NPPA belonging to the HIF-1 signaling pathway In conclusion, GEP of BM CD19+ cells demonstrated that 66 genes were robust biomarkers able to distinguish between WM and IgM-MGUS. The negative regulation of translation, pro-/anti-apoptotic processes, Notch signaling pathway, Purine/Pyrimidine metabolism, Sphingolipid metabolism and the regulation of transcription were mostly involved in the comparison between WM and IgM-MGUS. On the other hand, microarray of BM B cell clones (CD45+,CD38+,CD19+,LAIR-1-; CD27dim, IgM+, CD22dim, CD25+) identified groups of genes and pathways deregulated in the comparison between WM and IgM-MGUS. No statistically significant differences of expression were identified between WM vs. IgM-MGUS after correction for multiple testing. Moreover, fold-changes of transcripts whose p-value was lower than a nominal significance level of 5% were higher than 2 (absolute value) in only 4 out of 217 cases. We could suppose that the isolated clonal B-cell populations of WM and IgM-MGUS identified by flow citometry present similar expression signatures. Ongoing experiments of GEP of BM B cell clones vs. BM CD19 depleted cells of WM as well as IgM-MGUS could clarify the gene expression profiles of WM and IgM-MGUS. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Chronic myeloid leukemia (CML) is a disease of stemming from genetic damage to a hematopoietic stem cell. Despite nilotinib being a very effective drug for the treatment of CML, drug resistance can emerge. In the contest of the REL-PhilosoPhi34 study on behalf of the Rete Ematologica Lombarda, we performed an exploratory study aimed to identify the gene expression signature of bone marrow (BM) CD34+/lin- cells of CML patients at diagnosis as well as after 12 months of nilotinib to investigate the genes and pathways responsible for the response or resistance to nilotinib. Microarray experiments were performed using the latest generation Affymetrix GeneChip HTA 2.0 to further investigate genomic complexity. The study was developed in two steps as follows: In the first step we analyzed 30 CML patients and from the comparison between the BM CD34+/lin- cell counts from each patient at diagnosis and after 3 and 6 months of nilotinib, patients were divided into 2 classes: class 1 (n=24) showed highly reduced levels of CD34+/lin- cells while class 2 (n=6) demonstrated increasing levels of CD34+/lin- cells after 3 and 6 months of nilotinib, respectively (Fig.1). The 30 patients can be divided into 6 groups showing different patterns of CD34+/lin- cellularity (Figure 1). Data was preprocessed and normalized using Robust Multi-array Average (RMA) algorithm. The Significant Analysis of Microarrays (SAM) was used to identify genes with statistically significant changes in expression in CML patients. P-values were corrected for multiple testing using false discovery rate. No transcripts were selected as differentially expressed using this threshold. However, if a nominal significance level alpha equal to 0.05 is adopted together with a fold-change threshold equal to 2 (absolute value), 56 transcripts were selected in the comparison between the 2 groups of CML patients. Among them, we focused on NFKBIAgene which was over expressed in class 1 compared to class 2.NFKBIA is involved in 68 pathways regulating apoptosis (PI3K,NF-kB) and encodes IkBα protein belonging to the NF-kB pathway which is a potential downstream target of BCR-ABL1 due to its role in regulating cell survival and proliferation. Of note, 31/56 transcripts are located on chromosome 15 suggesting that this region could be crucial for transcriptional regulation of CML correlated to nilotinib response. The second step analyzed the GEP of CD34+/lin- cells of 8 patients at diagnosis (class 1) vs. the same 8 patients after 12 months of nilotinib (class 2) to investigate the gene expression changes and the pathways induced by nilotinib treatment. Data was preprocessed and analyzed as described above. 247 probes (corresponding to 51 genes and 180 non-annotated transcripts) were selected with a p-value lower than 0.05 after multiple testing correction and using a fold-change threshold equal to 3 (absolute value). Functional enrichment analysis was performed using DAVID and highlighted the following pathways and genes: ¯ Defense response and immune system: CAMP, CRISP3, CYBB, RGS1, CEACAM8, LTF (cell growth and differentiation), MNDA and HP (positive regulation of apoptosis) were over expressed in class 2 with high fold changes of 7.65, 9.95, 4.40, 3.47, 4.28, 3.61, 3.60, 3.95, respectively ¯ Transcriptional regulation and cell cycle: MMP8was under expressed in class 1 with the high fold change of 8,94 ¯ S100A12, S100A8, S100A9 (regulation of the cell cycle and differentiation) were over expressed in class 2 with the FC of 8.68, 3.96, 4.49, respectively ¯ RNA5S-ribosomal pseudogenes 310-311-312-313-314-315-316-317-320-363 were downregulated in class 2 ¯ 10 small-nuclear-RNAs and 11 small-nucleolar RNA were differently deregulated between the 2 classes ¯ APOC1, PLBD1 (lipid metabolism) were overexpressed and underexpressed in class 1, respectively In conclusion, this is the first study of GEP of CD34+/lin- cells with HTA 2.0 which demonstrated that 51 genes mostly involved in immune system, transcriptional regulation and cell cycle were significantly differently expressed from the comparison between CML patients at diagnosis and after 12 month of nilotinib. Ongoing studies are focused on examining the genetic and biologic mechanisms underlying nilotinib response or resistance to predict response or failure providing new insights into the molecular mechanisms in CML. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

We performed gene expression profiling of 36 symptomatic and asymptomatic WM patients, 13 IgM-MGUS cases and 7 healthy subjects as controls (CTRL) using Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. Bone marrow (BM) CD19+ cells (n=36) and BM CD138+ (n=32) cells were isolated from WM, BM CD19+ cells (n=10) and BM CD138+ (n=10) cells were selected from IgM-MGUS while BM CD19+ cells (n=7) and BM CD138+ (n=7) cells were isolated from CTRL. Data were preprocessed and normalized using RMA and ComBat. Selection and data analysis were performed using ANOVA followed by t-test, both adapted for microarrays and functional pattern discovery (Di Camillo et al., PLOS ONE 2012). The intersection between genes selected in WM vs. IgM-MGUS vs. CTRL in B cells are shown in Fig.1. The comparison between WM, IgM-MGUS and CRTLs CD19+ cells highlighted many differentially expressed genes. The comparison across all CD19+ cells identified 35 genes selected in all the comparisons (SET I); in particular, ADARB1 (alternative splicing), FANCI and ABCB1 (cell cycle), CRY1 and SATB1 (negative regulation of transcription) and ADAM23 (cell adhesion) were progressively underexpressed in CTRL vs. IgM-MGUS vs. WM. The comparison between WM vs. IgM-MGUS CD19+ cells demonstrated that a large number of genes (n=1687) were differently expressed, of which 632 specific for the WM vs. IgM-MGUS comparison and enriched for immune response, signal transduction, MAPK pathway, angiogenesis, cell cycle, transcription, alternative splicing and protein phosphorylation annotations. Among these, CXCL4, CXCL4L1, CD86 regulating immune response were underexpressed in WM B cells with the fold changes of 5.41, 4.11 and 2.93, respectively. RGS18, P2RY12, RAB27B (signal transduction) were downregulated in WM B cells with 5.42, 3.64, 3.10 fold changes, respectively. Regarding MAPK pathway, SYK was overexpressed while ADAM29, EGF, THBS1, PPM1L, FGFR1 were underexpressed in WM B cells. Genes involved in angiogenesis such as MEIS1, ANGPT1 were underexpressed, whereas PRKD2, CASP8, KRIT1 were overexpressed in WM B cells. SEPT8, SEPT6 were upregulated while PRKAR2B, SEPT5, PRR5 were underexpressed in WM B cells in the cell cycle. 104 genes regulating transcription were differently expressed in WM B cells and among them, 38 were zinc finger proteins. Among genes involved in protein phosphorilation, ITGB3, P2RY1, CTDSPL, KALRN, DAB2 were underexpressed while SGK494 and SIK3 were upregulated. The comparison between WM vs. CTRL as well as that between IgM-MGUS vs. CTRL of B cells showed that cell cycle, transcription, and cell adhesion differentiated WM and IgMMGUS in respect to CTRL. Of note, genes belonging to the cell cycle: ASPM, CENPE, ESCO2, CDCA2, MKI6, BUB1, CHEK1, CASC5 were progressively underexpressed in CTRL vs. IgMMGUS vs. WM with fold changes ranging between 3 and 5. LEF1, SSBP2, MYB, CDCA7L genes regulating transcription were progressively underexpressed in CTRL vs. IgMMGUS vs. WM with fold changes ranging between 2.5 and 4. RAG1 and IL4R (immune response) were underexpressed with high fold changes of 6.18 and 3.99 in WM vs. CTRL and with fold changes of 2.89 and 2.18 in the comparison of IgMMGUS vs. CTRL B cells, respectively. Intersection between genes selected in WM vs IgMMGS vs CTRL in CD138+ are shown in Fig. 2. A comparison across all CD138+ cells (Set I) showed that ESRP1 (RNA splicing), IGHD (immune response), TJP1 (apoptosis), and CADPS2 (protein transport) were progressively underexpressed in CTRL vs. IgMMGUS vs. WM with high fold changes ranging between 4 and 7. The comparisons between WM vs. IgM-MGUS and WM vs. CTRL highlighted that GBA3 belonging to sphingolipid pathway, SEPT10 and PARD3 (cell cycle), IGLC1 (immune response), NRG3 (regulation of cell growth) were underexpressed in WM. No genes were specific for the IgM-MGUS vs. CTRL comparison. In conclusion, the study demonstrated that many genes involved in alternative splicing, regulation of transcription, protein phosphorilation, cell cycle, and immune response were progressively underexpressed in CTRL vs. IgM-MGUS vs. WM in B cells. Conversely, the comparison between WM vs. IgM-MGUS as well as WM vs. CTRL plasma cells demonstrated that 12 genes were underexpressed in WM. In addition, microarray results showed that IgM-MGUS plasma cells seem to be similar to the normal cell counterpart. Disclosures: No relevant conflicts of interest to declare.

Abstract 3495 WM is a rare malignant B-cell disorder characterized by lymphoplasmacytic infiltration of the bone marrow (BM) and hypersecretion of monoclonal IgM. IgMMGUS is an asymptomatic condition characterized by the presence of a serum monoclonal IgM protein and bone marrow infiltration < 10%. WM (symptomatic and indolent) and IgMMGUS can be identified based on two main features, the bone marrow infiltration and the existence of signs and symptoms. The biological and genetic characteristics of both conditions need to be explored. Our study aims to highlight the different expression profiles between WM and IgMMGUS comparing CD19+ as well as CD138+ cells. We have investigated patients with WM (n =21) and patients affected by IgMMGUS (n=10). BM CD19+ and BM CD138+ cells were isolated from 21 WM patients, while BM CD19+ and BM CD138+ were isolated from 10 and 4 IgMMGUS patients, respectively. Microarray analysis was performed using Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. Data was preprocessed using Robust Multi-Array Average (RMA) software. Differential expression analysis was performed using Significant Analysis of Microarrays (SAM). Genes showing a q value lower than 5% and an absolute fold change greater than 2 were selected for further clustering analysis which was performed applying complete linkage hierarchical agglomerative clustering on Euclidean pairwise distances between genes. Microarray of WM vs IgMMGUS CD19+ cells has highlighted 151 differently expressed genes (Fig. 1). Among them we have found 33 genes involved in the regulation of transcription which were significantly overexpressed in WM vs. IgMMGUS. BM WM CD19+ cells overexpressed the following 14 Zinc Finger Protein (ZNF) genes: ZBTB40, ZNF83, ZNF137P, ZNF177, ZNF224, ZNF264, ZNF320, ZNF395, ZNF514, ZNF532, ZNF623, ZNF767, ZNF785, ZNF850. Other genes acting as regulators of transcription overexpressed in WM B cells were: HIF1AN, BHLHE41, EZH1, CCNL2, TCFL5, BLZF1, CIITA, PER2, MECP2, PRDM2, and ELP2. In particular, HIF1AN belongs to the PI3K/Akt/mTOR pathway, ELP2 is involved in the JAK/STAT process, and BHLHE41 and PER2 are included in the cicardian clock showing that these different biological mechanisms differently develop in WM with respect to IgMMGUS. TNFRSF10A, MAP4K4, TNFRSF10B, WNK1, DUSP22, ITPKB genes were overexpressed in WM B cells showing the involvement of Akt and MAPK signaling pathways which can play an important role in WM cells as they regulate several biological processes including cell growth, differentiation, survival, migration and metabolism. Gene expression profiling across CD138+ cells have demonstrated 43 differently expressed genes between WM vs. IgMMGUS (Fig. 2). MS4A1, BANK1 genes were overexpressed with high fold changes (FC) of 11.6 and 9.4, respectively, as well as GPR183, SWAP70 which were significantly overexpressed in WM vs. IGMMGUS, both genes showing a FC of 5. These results suggest that B cell activation and immune response are biological processes which act differently in WM compared to IgMMGUS. RALGPS2(FC=4), PLEKHG1 (FC=10) and ARHGAP24 (FC=4) genes mediating GTPase regulator activity of signal transduction were overexpressed in WM. ARHGAP24 could also be involved in the modulation of angiogenesis. FCRL2 and FCRLA genes involved in cell-cell signaling and cell differentiation, were significantly overexpressed in WM vs. IgMMGUS CD138+ with a fold change of 4.6 and 9, respectively. Again, the immune response seems to be a biological mechanism involved in WM CD138+ cells as CD79B (FC=5) and HLA-DOA (FC=4) genes were overexpressed in WM in respect to IgMMGUS. These differences may reflect varied immune mechanisms in the two disorders. In conclusion, the regulation of transcription, PI3K/Akt/mTOR and MAPK signaling pathways are the most relevant gene ontology biological processes occurring in CD19+ cells, while immune response, cell activation and signaling processes developing in CD138+ cells mainly distinguish WM and IgMMGUS. Future studies of the biological role of the genes differently expressed in WM vs IgMMGUS could clarify the pathogenetic processes underlying IgMMGUS and WM. The understanding of the molecular mechanisms leading to the progression of IgMMGUS to WM could help the identification of IgMMGUS patients who are at high risk for progression to WM. Fig. 1 GEP of BM WM CD19+ cells vs. IgMMGUS CD19+ cells. Fig. 1. GEP of BM WM CD19+ cells vs. IgMMGUS CD19+ cells. Fig. 2 GEP of BM WM CD138+ cells vs. IgMMGUS CD138+ cells. Fig. 2. GEP of BM WM CD138+ cells vs. IgMMGUS CD138+ cells. Disclosures: No relevant conflicts of interest to declare.

Abstract 4385 Introduction Conventional cytogenetic and FISH studies with the standard panel (13q14, 11q22.3, 17p13, 12, 6q23) are used to detect chromosomal abnormalities of clinical significance in patients with chronic lymphocytic leukemia (B-CLL). Recently, translocations and 5'IGH deletions involving chromosome14q32 are associated with B-CLL. Aim Our aim was to investigate the presence and the characteristics of chromosome 14q32 aberrations in a group of patients with B-CLL. Patients and Methods A total of 58 patients with B-CLL were investigated by conventional cytogenetic, in addition the cultures were stimulated with CpG oligodeoxynucleotide plus IL2. FISH studies with 14q32/IGH break-apart probe designed to detect chromosomal breakage of the IGH (14q32) locus, were done for 59 patients. Results Chromosomal abnormalities were detected in 60.3% of cases by cytogenetic. 14 patients showed complex cariotype while trisomy 12 was the most frequent anomaly found in 8 patients. Among the twelve other patients several chromosomal aberrations were noted: del(13)(q14), del(13)(q12q21), del(13)(q12q14), del(13)(q13q32), +21, t(11;13)(q23;q14), +12 t(1;4)(q31;p14), t(3;14)(p21;q22), del (11)(q12) +21, del(6)(q21), inv3(?p13q21), del(11)(q12). Abnormalities of chromosome 14 were found in 23.8% of patients. Deletion of 5'IGH, corresponding to the variable IGH segment, was the most frequent anomaly found in 8 patients. Interesting, a 3'IGH deletion was detected in two patients, while only one patient showed a complete deletion of chromosome 14 (47,XXY,add14q32). Three patients showed 14q32 translocations involving the IGH locus. Conclusions Based on our findings, deletions of the variable region of the IGH gene (IGHv) and 14q32/IGH translocations are involved in B-CLL. As these preliminary data are based on small sample size, our goal will be to study the cytogenetic profile of a large number of CLL patients. Future studies will permit the identification of 14q32 translocations and the type and the frequency of IGH rearrangements. Finally, chromosome 14 abnormalities will be correlated to other cytogenetic and FISH abnormalities, and associations with known prognostic markers, such as IGVH mutation status and ZAP-70 expression, will be investigated. Disclosures: No relevant conflicts of interest to declare.

Abstract 4388 Introduction Chronic lymphocytic leukemia (CLL) is a heterogeneous disease; ZAP-70 protein expression and IgVH mutational status have shown to be strong associated and to offer important prognostic information. Aim Our aim was to determine gene expression profiles of 46 CLL patients divided into three classes: group one (n=26) with mutated IgVH and ZAP-70-, group two (n=12) with unmutated IgVH and ZAP-70+, and group three (n=8) included CLL patients with unmutated IgVH and ZAP-70-, or mutated IgVH and ZAP-70+ respectively. Afterwards, in a second phase of the study, 16 other patients were investigated. Finally, the purpose was to define prognostic biomarkers and the biological pathways related to CLL. Patients and Methods We determined gene expression profiles using Affymetrix HG U133 Plus 2.0 in CD19+ leukemic cells. Differentially expressed genes were detected using ANOVA and t-test adapted for microarray data analysis and corrected for multiple testing using false discovery rate p-values. Subjects were clustered in groups with similar expression signature using cluster analysis (K-means, Euclidean distance). Results Statistical analysis revealed 154 differentially expressed probe-sets in the first (mutated IgVH and ZAP-70-) vs the second (unmutated IgVH and ZAP-70+) group, corresponding to 88 genes annotated in public databases. Interestingly, six genes were associated to the following biological pathways: MAPKsignaling (heat shock 70kD protein 8 HSPA8), B cell receptor signaling (ZAP-70, CKLF-like MARVEL transmembrane domain containing 3 CMTM3, dual adaptor of phosphotyrosine and 3-phosphoinositides DAPP1), Matrix Metalloproteinasis (transcription factor 20, TCF20), Apoptosis (X-linked inhibitor of apoptosis XIAP) and T cell receptor signaling (ZAP-70). In particular, ZAP-70, HSPA8, CMTM3 were significatively underexpressed while XIAP, TCF20 and DAPP1 were overexpressed in the first group of patients in comparison to the second group, respectively. Based on the expression of the 88 genes identified in the comparison between the first and the second group of patients, the 8 patients of the third class were divided in two clusters: 5 subjects were more similar to the first class, while 3 subjects appeared to be more similar to the second one. In particular, cluster analysis revealed that the 46 patients were better partitioned in two rather than in three classes, based on their expression profiles. 16 additional subjects were independently analyzed in a second phase of the project. Based only on the expression of the 88 genes previously identified, all of them were correctly classified in group one and group two. Further analysis was carried on the total of 62 subjects (n=46+16), dividing them in group A (n=17), showing deletion of 17p13 region and group B (n=45) without the deletion. Statistical analysis showed no correlation between groups A and B with respect to the previously defined group one, two and three. Moreover, no genes were identified as significantly differentially expressed in group A vs B. Conclusions In conclusion, our preliminary data revealed different gene expression signatures in B-cell chronic lymphocytic leukemia prognostic subgroups of patients, defined by IgVH mutational status and ZAP-70 expression. The functional pathways related to: MAPK signaling, B cell receptor signaling, apoptosis and T cell receptor signaling may ultimately influence CLL biology. Gene expression profiling studies are in progress on larger series of CLL patients in order to assess the association of the molecular signature, based on the identified genes and their pathways, with respect to prognostic information. No different gene expression signatures appeared considering CLL patients divided in two groups differing from the presence or the absence of deletion of 17p13 region respectively Disclosures: No relevant conflicts of interest to declare.