Wier, Scott Van

Abstract Despite recent advances and improvement in treatment options, chronic lymphocytic leukemia (CLL) is still an incurable disease. Recent advancement in parallel sequencing technology has provided better understanding of genetic landscape and clonal architecture underlying disease course. Furthermore, mutation status of TP53, SF3B1, NOTCH1, and BIRC3 has improved current CLL risk stratification. In order to better understand the genetic basis and clonal evolution underlying disease progression we analyzed a homogeneously treated cohort of CLL cases at multiple time points before and after therapy. We performed WES in 30 sequential samples from 12 CLL cases. All 12 cases received PCR (pentostatin, cyclophosphamide and rituximab) based chemoimmunotherapy as initial treatment. Of these 12 cases, two had samples available both >6 months prior to enrollment and at treatment initiation, seven patients who experienced relapse had samples available at both treatment initiation and at and the time of relapse, and three patients had samples >6 months prior to enrollment, at treatment initiation, and at relapse. These cases were previously characterized by aCGH/FISH. R function Mclust and kernel density estimate (KDE) plots were used to identify subclonal heterogeneity. An average of 102-fold depth coverage was obtained with an average of 80% of the targeted regions being covered by at least 30X. Overall, we detected a total of 219 nonsynonymous Single Nucleotide Variants (SNV) and indels (average 19, range 11-33). Del13q (42%) was identified as most prevalent copy number abnormality followed by trisomy 12 (33%), del11q and del17p (17% each). Recurrent mutations were identified in NOTCH1 (33%), DDX3X (25%), TP53, SF3B1 XPO1 and MED12 (17% each). Other tumor implicated genes such as CARD11, NOTCH2, NOTCH4, DIS3, TRAF2, NFKBID, CHK2 and RB1 were found mutated in individual cases. In all 12 cases we identified at least one relevant cytogenetic abnormalities (del11q32, del13q14, del17p13, trisomy 12) and/or a mutated driver gene. Interestingly we found NOTCH1, DDX3X and XPO1 mutated at a significantly higher prevalence than previous studies (10%, 2% and 4%, respectively). Since all cases in our cohort correspond to individuals with progressive disease, a plausible explanation for the higher prevalence of mutations in our cohort is that these mutations are associated with disease progression. By WES we identified multiple subclones in 58% of cases (7 of 12 cases), as compared to previously identified 33% (4 of 12 cases) by CGH/FISH. In 5 chemotherapy naïve cases, no significant increase in genomic complexity was detected with disease progression. In contrast, 5 out of 10 cases with samples analyzed before CIT and at the time of relapse had changes in clonal dynamics, with initially dominant subclones diminishing in response to therapy and the available empty niche being occupied by expansion of another “fitter” subclone at the time of relapse. No acquisition of tumor associated gene mutations was seen post therapy or associated with relapse in 9 of 10 cases. In the remaining case we identified an interesting example of convergent evolution. The pre-therapy sample was comprised of a unique clone characterized with del11q13, mutations in SF3B1 (G742D) and DDX3X (pE196fs). After therapy that clone was either eradicated or reduced to below detection level. Interestingly another clone emerged after relapse that harbored an independent del11q22 and mutations affecting different aminoacids of SF3B1 (K700E) and DDX3X (pL21fs). The strong selection of impairment in these genes during disease progression suggests a key role of these pathways/genes in the pathogenesis of this patient. In conclusion, these studies provide additional insight into the genomic landscape and clonal architecture in a cohort of homogeneously treated CLL cases before and after therapy. Stable clonal architecture was seen in prior to treatment, whereas clonal evolution leading to increased tumor heterogeneity occurred after the under selection selective pressure of therapy in relapsed cases. Early identification of tumoral heterogeneity and underlying genetic abnormalities might provide better targets for therapy to decrease the likelihood of relapse. Disclosures: Fonseca: Medtronic: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; Genzyme: Consultancy; BMS: Consultancy; Lilly: Consultancy; Onyx: Consultancy, Research Funding; Binding Site: Consultancy; Millennium: Consultancy; AMGEN: Consultancy; Cylene: Research Funding; Prognostication of MM based on genetic categorization of the disease: Prognostication of MM based on genetic categorization of the disease, Prognostication of MM based on genetic categorization of the disease Patents & Royalties. Shanafelt:Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding.

Abstract Abstract 2838 The combination of novel agents and chemo-immunotherapy (CIT) has led to a significant increase in both overall and complete response rates for chronic lymphocytic leukemia (CLL). There is, however, significant clinical heterogeneity in the overall effectiveness of CIT. Specifically, some patients are long-term responders who experience durable clinical remission while others, even though responding well initially, have less durable remissions. It is unclear if this heterogeneity is a reflection of an incomplete cytotoxic effect or whether certain leukemia sub-populations (i.e. subclones) are resistant to the effects of CIT. Comprehensive analysis of tumor cells has provided, in some malignancies, evidence of extraordinary genomic complexity and intraclonal genetic diversity. Conversely, little is known of the genetic architecture of the leukemic clone in CLL patients and the extent to which CLL tumor cells differentiate genetically at the subclonal level. In order to gain insight into the genetic architecture and clonal evolution underlying disease progression/relapse, we performed a sequential, longitudinal whole genome analysis by array-based comparative genomic hybridization (aCGH) in 22 CLL cases with evidence of clinical progression and/or clinical relapse post therapy. These patients received standard purine analogue, cyclophosphamide and rituximab (PCR) for progressive disease. For the purposes of this study, we classified samples as; time point 1 (TP1) collected >6 months before starting first-line CIT for disease progression; time point 2 (TP2) consisting of tumor samples at the time of initial progression requiring treatment; and time point 3 (TP3) consisting of tumor samples collected >6 months after initial treatment but prior to subsequent treatments. Overall, aCGH assay was performed in 54 samples from 22 cases (2 to 4 time points analyzed per patient): 6 cases were analyzed at TP1 and TP2, 8 at TP2 and TP3, 2 at TP1 and TP3, and the remaining 6 cases were analyzed at TP1, TP2 and TP3. In cases showing genetic changes between time points, FISH analyses were performed in 32 additional samples to assess intra-clonal diversity and provide a quantification of the clone size. There was a small increase in genomic complexity in later time points with a mean of 4 (median 4, range 0–12) CNAs detected at the first time point for each patient, increasing to a mean of 5.3 (median 4.5, range 0–21) CNAs at the last time point analyzed. Diverse patterns of changes in genetic architecture were observed. The simplest change in genetic architectures was the linear sequence, found in 2 cases (9%) and characterized by the maintenance of the initial abnormalities and the subsequent acquisition of additional CNAs in the same subclone. The pattern of changes in genetic architecture in another 4 patients (18%) demonstrated marked heterogeneity including multiple genetic subclones evolving either in succession or in parallel. In all 4 cases with multiple subclones, a marked shift in the dominant subclone was observed at the time of disease relapse, with the minor subclones at TP1 and TP2 becoming the dominant subclones at TP3. In addition, the subclonal evolution continued following secondary therapies, and in all 4 cases the emerging subclone showed resistance to multiple treatments. This argues strongly in favor of subclones with specific genetic structures becoming dominant with the selective evolutionary pressure imposed by therapy. In summary, this sequential genetic study reveals that CLL leukemic B cells exhibit an unanticipated level of heterogeneity including a variable number of genetically distinct subclones, which display dynamic behavior in disease progression. The characterization of this intraclonal architecture is of critical importance to better understand the mechanisms of disease progression and relapse as well as to ultimately inform us of individualized treatment strategies. Disclosures: Fonseca: Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.

Abstract Abstract 2869 Background: Chromosome abnormalities are universal in multiple myeloma (MM) and will ultimately categorize patients into hyperdiploid and non-hyperdiploid MM. Among non-hyperdiploid patients those that exhibit hypodiploidy have the most aggressive clinical phenotype. What genetic features are unique to hypodiploidy are not fully described. Therefore, we performed a comprehensive high-resolution analysis to differentiate and characterize hypodiploid MM. Materials and methods: MM patients were analyzed using a combination of array-based comparative genomic hybridization (aCGH) (n=275) and gene expression profiling (GEP) (n=239). Agilent 244K and Affymetrix U133A Plus 2.0 arrays were used in the aCGH and GEP experiments, respectively. Hypodiploid MM was differentiated using pseudokaryotyping based on aCGH findings. Samples estimated to have less than or equal to 44 chromosomes were designated hypodiploid, 45–47 chromosomes were nonhyperdiploid and greater than or equal to 48 and less than 74 chromosomes were considered hyperdiploid. Using GEP the main gene indices and signatures associated with outcome were determined including the translocation and cyclin D (TC) classification, UAMS 70-gene index, proliferation index, centrosome signature and NF-kB index. Differentially expressed genes were also investigated. Results: A total of 53 (19%) MM patients were classified into the hypodiploid group, mainly characterized by monosomies of chromosomes 13 (83%), 14 (42%), 22 (23%) and × (50%) (females) with p and/or q-arm aberrations including gains of 1q (51%) and 8q (25%) and losses of 1p (49%), 4p (21%), 4q (23%), 6q (38%), 8p (34%), 12p (25%), 12q (26%), 14q (32%), 16p (25%), 16q (51%) and 17p (25%). Patients with loss of 1p were associated with 4p- (p <0.029), 4q- (p<0.0001), 12p- (p<0.007), 12q- (p<0.0002), any 14 (p<0.022), 16p- (p<0.005), 16q- (p<0.001), and monosomy 22 (p<0.024). Patients with loss of 17p were associated with 12p- (p<0.025), 12q- (p<0.039) and 16q- (p<0.031). The main gene indices and signatures in MM showed that nearly one half of the hypodiploid patients having high-risk disease, ranging from 45% with a high 70-gene index, 47% with high centrosome signature and 51% with a high proliferation index. In addition, hypodiploid patients also displayed a translocation type signature in the TC classification defined by 11q13 (24%), 4p16 (24%) and maf (12%). Overall, 253 genes have >2 fold expression change comparing hypodiploid vs. hyperdiploid including a five fold decrease in the heparin-degrading endosulfatase gene SULF2, a decrease of genes in the TGF-b signaling pathway (MYC, ID3, SMAD1, LTBP1) and those involved in Wnt signaling (DKK1, FRZB). Up regulated genes included those from the p53 signaling pathway and cell cycle (CCND2, CDKN1C, RPRM), cell adhesion molecules (ITGB8, CD28) and tight junction pathway (RRAS2, RRAS, CSDA). Conclusion: This represents the most comprehensive genomic characterization of hypodiploid MM to date. These cases exhibit a high propensity for high-risk gene expression profiles and have a high prevalence of −13, −14, 1q gain and 1p loss as predicted. Given our findings it is likely that hypodiploid is not a separate category but rather the genetic “phenotype” of a more advanced clone. Today, using these two platforms together in a routine setting would provide the most comprehensive genetic analysis, important for individualized therapeutics. Disclosures: Fonseca: Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.

Abstract Primary central nervous system lymphoma (PCNSL) is an aggressive and incurable variant of non-Hodgkin lymphoma (NHL) that is confined to the central nervous system. Most PCNSL (90%) are part of the immune-privileged site-associated diffuse large B-cell lymphomas (DLBCL). Unlike nodal DLBCL, only a limited number of genetic studies have been performed in PCNSL, partly due to lack of available tissue specimens. Because of the fragmented knowledge of the genomic basis, it is still a matter of debate whether they differ from systemic DLBCL with respect to their molecular features and pathogenesis and also if there is a CNS specific signature. We performed a comprehensive genomic study in a cohort of 22 immunocompetent (HIV- and EBV-) PCNSL. DNA was extracted from FFPE tissues (N=15) and frozen tissue (N=7). Samples were analyzed using a combination of aCGH (N=18), exome sequencing (N=10), mate-pair genome sequencing (N=2) and targeted sequencing (N=8). We found a complex karyotype with a median of 21 copy-number abnormalities (range 10–47), 6 structural abnormalities, 6 frameshift indels and 67 nonsynonymous mutations. By integrating mutation and copy number data we found a group of genes recurrently mutated and/or deleted in PNCSL. Remarkable findings were the high prevalence of MYD88 activating mutations (L265P/M232T/V217F), found in 69% of cases and the biallelic loss of CDKN2A (60%). A subset of recurrent abnormalities was exclusively found in PCNSL, and not being previously identified in systemic DLBCL. Thus, 11% of PCNSL have biallelic inactivation of TOX (a regulator of T-cell development) and PRKCD (protein kinase C delta), while another 17% of cases show focal monoallelic deletions/mutations in these genes. Finally, recurrent mutations have been identified in ATM, which have not been found in nodal DLBCL. Several other genes affected have been previously identified in nodal DLBCL, such as biallelic loss of TNFAIP3 (16%), PRDM1 (16%), GNA13, TMEM30A, B2M and CD58 (11% each), activating mutations of CD79B (28%) and CARD11 (19%) and translocations of BCL6 (22%). Components of the NF-kB pathways were altered in >90% of PNCSL. Pathway analysis also showed an enrichment of networks associated with immune response, proliferation, regulation of apoptosis and lymphocyte differentiation and activation. Finally, we searched for associations between genetic alterations and clinical outcome. We showed that deletions of 6q21 (PRDM1) and 6q23 (TNFAIP3) were both associated with shorter overall survival (p=0.007 and p=0.03, respectively). In summary, we report a genomic background in PCNSL similar to post-GC DLBCL but reinforcing the existence of a subset of abnormalities specific to PCNSL, suggesting their potential relevance in the disease pathogenesis. Additionally, the results obtained from FFPE samples are encouraging and larger archival tissue collections can now be analyzed in order to complement the still fragmented knowledge we have of the genetic basis of the disease. Disclosures: Stewart: Onyx: Consultancy, Research Funding; Millenium: Honoraria, Research Funding; Celgene: Honoraria; BMS: Honoraria. Fonseca:Medtronic: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; Genzyme: Consultancy; BMS: Consultancy; Lilly: Consultancy; Onyx: Consultancy, Research Funding; Binding Site: Consultancy; Millennium: Consultancy; AMGEN: Consultancy; Cylene: Research Funding; Prognostication of MM based on genetic categorization of the disease: Patents & Royalties.

Abstract Abstract 3605 CLL is a malignant B-cell disorder characterized by the accumulation of small B lymphocytes with a mature appearance in blood, marrow and lymph nodes. Despite effective treatment options, all patients with CLL will eventually relapse after therapy. This could be due in part to the presence of subclones of the CLL cell population that harbor genetic abnormalities, which confer resistance to treatment. The aims of this study were to investigate the clonal evolution in longitudinal samples of CLL patients and to identify genetic alterations associated with disease progression and resistance to therapy. Sequential analyses were performed in 51 samples from 23 patients who were included in a previously reported clinical trial of pentostatin, cyclophosphamide and rituximab (PCR) given every 3 weeks for 6 cycles in previously untreated CLL (Blood 109:2007). In all cases the first sample analyzed was prior to therapy. In 5 of 23 patients, three time points were analyzed: >6 months prior to entry onto PCR trial (time point A), just before starting with the PCR regimen (time point B), and the time of relapse after PCR trial (time point C). Seven patients were analyzed at time points A and B; 9 at time points B and C and 2 at time points A and C. The median time between points A and B was 17.5 months (range 8–48 months) and between points B and C was 20.5 months (7–60 m). All samples were examined by array-based comparative genomic hybridization (aCGH) using the Agilent Sureprint G3 (1 million probe) array. aCGH findings were confirmed by interphase FISH using probes for D13S319 (MIR16–1/MIR15A), RB1, MDM2, CEP12, CEP6, MYB, TP53, NFKBIA, PERP and FGFR1 loci. Overall, we observed a small increase in the number of copy-number abnormalities (CNA) with disease progression. Twenty-two of the 23 patients with paired samples harbored at least one CNA that persisted in all samples, indicating clonal relationship between the sequential samples. In 15 of the 23 patients the tumor clone was stable and no CNA differences between time points were identified. Conversely, genomic evolution was found in 8 patients. In 3 cases the genetic differences were observed pre treatment (between time points A and B) and in the other 5 cases, the observed changes were found after therapy (between time points A and C or between B and C). One remarkable case with genome evolution exhibited two subclones sharing trisomies 12 and 19, but with several unique CNA confined to each subclone. The first subclone was characterized by deletions of 6q, RB1, MIR16-1/MIR15A and 3 other losses, while the second subclone showed homozygous deletion of MIR16–1/MIR15A and 5 other monoallelic deletions. The first subclone was predominant at time points A and B (60–70% of cells), but was present in only 10–20% of cells at time point C as confirmed by FISH. Conversely, the second subclone was observed in ~20% of cells at time points A and B and became predominant after therapy, found in ~80% of cells at time point C. Another case was characterized by deletion 11q32 (including ATM and others) as the sole abnormality at time point B. Significant genomic complexity was observed at time point C, including deletions of 11q32, 9p21 (CDKN2A), 9q12-q33, 14q13.2 (NFKBIA) and 17p (TP53), and gains of 2p16 (REL) and 9q34. Interestingly, the deletion 11q32 from both time points arose independently at each time point, as they exhibited different chromosomal breakpoints and copy number variants. Moreover, the other CNA found at relapse were not identified at diagnosis (confirmed by aCGH and FISH). For evolution of specific CNA, trisomy 12 was found in 5 cases at the first sample analyzed and was stable with no changes between time points. The frequency of deletions 13q14.3 (MIR16-1/MIR15A) and 17p increased at the later time points. Conversely, –6q decreased in frequency across time points (3 cases in time points A–B and 1 case in time point C). In summary, at least 35% of CLL patients exhibited clonal evolution and at least 9% showed evidence of multiple subclones. This subgroup of CLL patients provides an exceptional framework for comprehensive analysis of genome evolution during disease progression before and after therapy. Our observations also support the hypothesis of a common CLL progenitor cell can give rise to clonally related, but genetically evolving subpopulations of tumor cells. Finally, this study may bring novel information regarding the drug resistance pathways utilized by CLL B cell clones post therapy. Disclosures: Kipps: GlaxoSmithKline: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Genzyme: Research Funding; Memgen: Research Funding; Igenica: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi Aventis: Research Funding; Abbott Laboratories: Research Funding. Fonseca:Genzyme: Consultancy; Medtronic: Consultancy; BMS: Consultancy; AMGEN: Consultancy; Otsuka: Consultancy; Celgene: Consultancy, Research Funding; Intellikine: Consultancy; Cylene: Research Funding; Onyx: Research Funding; FISH probes prognostication in myeloma: Patents & Royalties.

Abstract Abstract 1923 Our understanding of the genetic abnormalities associated with the development of multiple myeloma has increased significantly in the last decade. However, very little is known about how, or if, myeloma tumor genomes change with time and if therapeutic interventions influence these events. To address these issues we studied a cohort of 29 patients for whom at least two serial samples (1-65 months, median 19 months) were available for analysis. Each serial pair was analyzed by both array-based comparative genomic hybridization (aCGH) and microarray gene expression profiling (GEP) to identify DNA copy number abnormalities (CNA) at a 25kb resolution and gene expression differences present in the bulk of the tumor mass. Though this does not address the intra-clonal heterogeneity that may exist at a given time point, it does answer if the bulk of the tumor mass is changing with time. This study has unearthed several surprising and clinically relevant findings. First, myeloma tumor genomes are not as unstable as previous cytogenetic analyses suggest. In 40% of patients we observed no detectable CNA changes (1-37 months, median 12 months). In 24% of patients we observed the exclusive acquisition of new CNA (1-12, median 3.5) (3-22 months, median 18 months). In 36% of patients we observed both the loss (1-20, median 3) and gain (1-33, median 21) of CNA (5-43 months, median 20 months). Because time was not a significant influence on the detection of stable or unstable genomes we compared CNA changes with TC class and found patients with the high-risk 4p16 and maf IgH translocations were over-represented in the latter subset of patients. These observations raise the question of what happens between multiple rounds of therapy and if different regimens influence these phenotypes differently. For two patients with no CNA changes between the first two time points there was an additional sample that extended the follow-up by 52 and 12 months. Again no CNA changes were seen between diagnosis and these final samples taken 63 and 50 months later. For one patient with CNA changes (5 shared, 29 lost, and 32 gained) we have a detailed time course of 5 samples from diagnosis through to end-stage plasma cell leukemia. This patient received continuous lenalidomide-dexamethasone (Rd) for 20 months and progessed with a clone containing a BIRC2/3 deletion, which activates the NFKB pathway. The patient received single agent PR-171 and a bortezomib containing regimen and unexpectedly, the tumor genome observed in the third sample was almost identical (32 shared, 2 lost, and 4 gained CNA) to the first time point, including two copies of BIRC2/3. Subsequently, the patient received melphalan-prednisone-bortezomib (MPV) and the tumor genome observed in the fourth and fifth samples, which were identical, were similar to that seen in the second sample (24 shared, 13 lost, and 39 gained CNA). To understand these observations better we performed FISH to ascertain if the observed clones were detectable earlier, albeit at a low frequency. These experiments proved that the two dominant subclones observed at time points 1 and 3 versus 2, 4, 5 were mutually exclusive at the single cell level. Moreover, both of these clones were detectable at diagnosis with 12% of the tumor mass being the second subclone that eventually evolved into plasma cell leukemia. Interestingly, we assayed 5 of the 39 unique CNA observed in the final two samples and only one, the 17p13 deletion, was detectable earlier. This suggests the MPV regimen effectively eliminated a clone that was previously sensitive to Rd and selected for a dramatically evolved subclone that was previously sensitive to two different proteasome inhibitors. Although it is clear that the high-risk patients are enriched in the subset with the most changes, it is not clear if the specific drugs used (Melphalan vs IMID vs proteasome inhibitor) or intervention strategy (Cycled vs continuous/maintenance) and perhaps the response achieved (PR vs CR) influences these events. These observations do highlight two important clinical concepts that need to be considered in the future. First, the meaning of a partial response needs further investigation as this may reflect effective elimination of one subclone but not another. Second, because some patients are not changing or can revert back to a previous subclone we need to consider re-chanllenging patients with previously effective regimens when patients progress. Disclosures: Fonseca: Genzyme: Consultancy; Medtronic: Consultancy; BMS: Consultancy; AMGEN: Consultancy; Otsuka: Consultancy; Celgene: Consultancy, Research Funding; Intellikine: Consultancy; Cylene: Research Funding; Onyx: Research Funding; FISH probes prognostication in myeloma: Patents & Royalties. Stewart:Millennium: Consultancy; Celgene: Honoraria. Bergsagel:Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Millennium: Speakers Bureau; Novartis: Speakers Bureau.

Abstract Abstract 1246 Poster Board I-268 B-cell chronic lymphocytic leukemia (B-CLL) is characterized by a variable clinical course. Genomic aberrations as detected by FISH analysis and the mutational status of the IgVH genes have been found to have prognostic clinical significance with unmutated IgVH genes associated with clinically more aggressive disease. Previous studies have suggested that unmutated IgVH B-CLL is associated with increased chromosomal instability and increased chromosomal aberrations leading to accelerated disease progression. We recently performed a high-throughput, high-resolution array-based comparative genomic hybridization (aCGH) in a cohort of B-CLL patients. Fifty-eight patients have been analyzed by using the Sureprint G3 microarray (one million probes, Agilent). Our working hypothesis was that we would see increased genomic aberrations and instability in the unmutated IgVH group. The total number of copy number aberrations and total megabases (Mb) deleted or gained per genome were used as a surrogate marker for genomic instability. Variations in chromosomal aberrations in various VH families were also evaluated. B-CLL with unmutated IgVH genes utilized members of the VH1, VH3, VH4 and VH5 families with VH1-69 the most prevalent. B-CLL with mutated IgVH genes utilized VH families VH1, VH2, VH3, VH4, and VH6. Considerable variation was seen in the number of genomic abnormalities between cases. Overall, 315 abnormalities have been identified (236 deletions and 79 gains), with a median of 4 copy-number abnormalities per patient (range 0-32). In patients with IgVH mutational status available the median number of chromosomal aberrations in the mutated group was 5 (range 1-17) and unmutated group was 4 (range 0-20). Copy number deletions were significantly more common than chromosomal gains for the total group and in both the unmutated and mutated IgVH gene groups. Mean number of megabases (Mb) of DNA deleted in the unmutated IgVH group was 17.80Mb (standard deviation 24.17 Mb, median 6.63 Mb, range 0 - 90.55Mb) and 40.65 Mb in the mutated IgVH group (standard deviation 86.04 Mb, median 15.35Mb, range 0 – 307.57Mb). Mean number of Mb gained in the unmutated group was 37.74Mb (standard deviation 57.58Mb, median 0.44Mb, range 0 – 132.47Mb) and 32.99Mb (standard deviation 59.76Mb, median 0.14Mb, range 0 – 172.50Mb) in the mutated group. Nine patients had B-CLL with VH1-69 genes and 8 of them were unmutated. Median number of chromosomal deletions was 2 and gains also 2 (ranges 0 – 8 deletions and 0 – 4 gains) for this group. Similar findings were seen for the other VH families. These results initially suggest that total copy number variations do not account for the observed clinical differences between mutated and unmutated IgVH B-CLL. The catalogue of potentially clinically relevant chromosomal abnormalities found in this aCGH study is being currently evaluated. Disclosures Zent: Genentech, Bayer, Genzyme, Novartis: Research Funding. Shanafelt:Genentech: Research Funding; Hospira: Membership on an entity's Board of Directors or advisory committees, Research Funding; Polyphenon E International: Research Funding; Celgene: Research Funding; Cephalon: Research Funding; Bayer Health Care Pharmaceuticals: Research Funding. Kay:Genentech, Celgene, Hospira, Polyphenon Pharma, Sanofi-Aventis: Research Funding; Biogenc-Idec, Celgene, Genentech, genmab: Membership on an entity's Board of Directors or advisory committees. Fonseca:BMS: Consultancy; Amgen: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; Medtronic: Consultancy; Genzyme: Consultancy.

Abstract Abstract 2406 The outcome for a given CLL patient is difficult to predict. While there are promising models, they require collation of multiple clinical and laboratory parameters, and it remains to be seen whether they will apply to typical CLL patients in the community. To further dissect out explanations for this dramatic clinical heterogeneity, we sought to understand genomic complexity of clonal B-cells as a possible explanation of clinical variability with specific application to genomic complexity as a predictor of therapeutic response and clinical outcome in CLL. Thus we wished to identified global gains and losses of genetic material in order to define copy-number abnormalities (CNA) in 48 clinically progressive CLL patients who were about to be treated on a chemoimmunotherapy protocol. This protocol was previously reported by us (Blood. 109:2007) and had an induction phase with pentostatin (2 mg/m2), cyclophosphamide (600 mg/m2) and rituximab (375 mg/m2) given every 3 weeks for 6 cycles and then responding patients were followed ever three months until relapse. In order to estimate CNA, we employed array-based comparative genomic hybridization (aCGH) using a one-million oligonucleotide probe array format on the leukemic B-cells from the 48 patients entering this trial. In those same patients, the aCGH data were compared to a) FISH detecxtable data using a panel for the common recurring genetic defects seen in CLL and b) to their clinical outcome on this trial. With aCGH we found that 288 CNA were identified (median of 4 per patient; range 0–32) of which 215 were deletions and 73 were gains. The aCGH method identified most of the FISH detected abnormalities with a complete concordance for 17p13.1- deletion (17p-) between aCGH and FISH. We also identified chromosomal gain or loss in ≥6% of the patients on chromosomes 3, 8, 9, 10, 11, 12, 13, 14 and 17. We found that CLL patients with ≥15 CNA had a significantly worse progression free survival (PFS) than patients with <15 CNA (p=0.004)(figure). Patients with ≥15 CNA also had a shorter duration of response than those with <15 CNA (p=0.0726). Of interest, more complex genomic features were found both in patients with a 17p13.1 deletion and in more favorable genetic subtypes such as 13q14.1. Thus, for 5 patients with >15 CNAs the following FISH patterns were seen: +12/13q14.1-x1/13q14.1 -x2, 13q14.1 ×1 (n=2), and 17p13.1 (n=2). In addition, a 17p- by FISH was positively associated with the number of CNA and total deletion size. The odds of having an overall response decreased by 28% (95% CI: 5–55%; p=0.015) with each additional CNA for the 17p13.1- patients. In addition to defining genomic complexity as the total number of CNA for each patient, we also defined complexity as the sum of the lengths of all interstitial chromosomal gains and losses. When defined as the total size of chromosomal gains or losses, genomic complexity was significantly associated with 17p13.1 and worse overall clinical response. In summary, this analysis utilized the global assessment of copy number abnormalities using a high-resolution aCGH platform for clinically progressive CLL patients prior to initiation of their treatment. One outcome was that we found higher genomic complexity was associated with shorter progression-free survival, reduced duration of response and predicted a poor response to treatment. In addition since we did find genomic complexity in more traditionally favorable FISH categories, such as 13q14.1 type defects, this may explain why some of the latter patients do not fare as well as might be expected even with aggressive chemoimmunotherapy approaches. This study adds information on the association between inferior trial response and increasing genetic complexity as CLL progresses. Disclosures: Off Label Use: Pentostatin. Kipps: GlaxoSmithKline: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Genzyme: Research Funding; Memgen: Research Funding; Igenica: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi Aventis: Research Funding; Abbott Laboratories: Research Funding.

Abstract In an attempt to identify novel genetic abnormalities in multiple myeloma (MM) we analyzed 68 MM patient samples and 42 human myeloma cell lines (HMCL) with high-resolution (60kb) array-based comparative genomic hybridization (aCGH). We concentrated on bi-allelic genomic deletions with the expectation that these regions might contain novel tumor suppressor genes and identified a common abnormality at 14q32. Fine mapping of the 14q32 microdeletion by PCR demonstrated a 45 kb minimal deletion encompassing two genes; TRAF3, a negative regulator of the non-canonical NF-kB pathway, and AMN. To determine the frequency of copy number abnormalities of this region a cohort of 161 patients were screened with a FISH probe mapping to the minimally deleted region. This identified 7 (4.4%) patients with bi-allelic deletion and 19 (11.8%) patients with a single copy of the target region. Sequencing of all TRAF3 coding exons in 62 patients and 42 HMCL identified inactivating mutations in 9 patient samples and 4 HMCL. Overall, the incidence of TRAF3 inactivation, bi-allelic deletion or mutation, is 7/42 (16.7%) in HMCL and 12/62 (19.4%) in patients, making it one of the most common genetic abnormalities identified in MM. Importantly, dysregulation of NF-kB pathways is a common event in MM, but the mechanism for this dysregulation is largely unknown. Although TRAF3 is a negative regulator of the non-canonical NF-kB pathway it has not previously been implicated in cancer. Therefore, we tested the tumor suppressor function of TRAF3 by reintroducing TRAF3 into HMCL harboring TRAF3 abnormalities using an adenovirus gene transfer system. Expression of TRAF3 in HMCL with TRAF3 abnormalities rapidly reversed cellular processing of NFKB2 from p100 (an inactive isoform) to p52 (active) and additionally led to a 60% reduction in cellular proliferation (range, 10–89%) associated with Go/G1 cell cycle arrest and induction of cell death by apoptosis. At 48 hrs post infection the proportion of cells entering S-phase or G2/M declined by 31% (range, 8.1–56.1) and the proportion undergoing apoptosis increased by 28% (range 11.7–47.4%). In contrast, over-expression of TRAF3 in control HMCL without TRAF3 abnormalities had no effect. Overall, TRAF3 is identified as a novel tumor suppressor that regulates the non-canonical NF-kB pathway and is inactivated in ~ 20% of MM patients. Given the involvement of the NF-kB pathway in multiple human malignancies and the near constitutive expression of TRAF3 we propose that inactivation of TRAF3, a novel tumor suppressor identified for the first time in this study, may occur in other cancers. In this regard, we have recently identified a Burkitt lymphoma cell line that does not express TRAF3 and thus has constitutive processing of p100 to p52. The clinical and therapeutic consequences of this finding and its frequency in other tumor types are currently the focus of further investigation. In conclusion, the abnormalities identified in this study represent the first mutations identified that cause constitutive NF-kB activation in MM.

Abstract The t[4;14] simultaneously deregulates fibroblast growth factor receptor 3 (FGFR3) and MMSET in 15% of patients (pts) with Multiple Myeloma (MM). Although oncogenic, FGFR3 expression is lost in ~25% of t[4;14] pts on gene expression profiling while MMSET is always retained. The t[4;14] is highly associated with poor prognosis, furthermore FGFR3 kinase inhibitors are in Phase I trials, thus detection of t[4;14] pts is increasingly important. The Multiple Myeloma Research Consortium (MMRC) therefore evaluated 4 competing methodologies for the diagnostic detection of t[4;14]. Bone marrow (BM) samples were collected in uniform fashion from 85 pts. FGFR3 immunocytochemistry (ICC) and flow cytometry (FC) were performed at the retrieval site, while split samples were shipped to the MMRC tissue bank and processed under GLP conditions. Processed samples were analyzed by cIg-FISH and quantitative IgH-MMSET RT-PCR (QPCR) on unsorted BM and blood. Of 60 samples with sufficient plasma cells for analysis, 8 were FISH +ve [13.3%]. Low % BM plasma cells prevented successful FISH in 25% of samples reflecting large volume harvests and hemodilution. Analysis was conducted in a blinded fashion. With FISH as the gold standard for detection of t[4;14] the sensitivity and specificity of the other diagnostic tests are: BM QPCR is most sensitive (7 of 8 FISH positive detected) and specific (42/43 negatives correctly identified). QPCR on peripheral blood is ongoing. FC is more sensitive to the detection of FGFR3 protein than ICC. Correlation between FC and ICC was only 0.46. Interestingly, 6 of 7 evaluable pts here and 13/14 (92%) in an expanded analysis of FISH +ve patients also expressed the FGFR3 protein. Given the discrepancy between this finding and previously reported loss of FGFR3 expression in 25% of pts we further explored t[4;14] stability. Clonal selection/heterogeneity was determined by the % plasma cells in each pt. with an unbalanced translocation (loss of one der chromosome). In 42 t[4;14] pts, 13 (31%) had a balanced translocation(>75% of cells with a double fusion), 14 (33%) had an unbalanced translocation (>75% cells with only one signal), and 15 (36%) had a chimeric picture. This heterogeneous pattern is suggestive of an evolution towards an unbalanced translocation. Despite this survival did not differ between pts with balanced or unbalanced translocation. Conclusions: These results indicate that the t[4;14] is highly heterogeneous with respect to balanced versus unbalanced translocations, which likely evolve over time. For detection, cIg-FISH continues to be the gold standard diagnostic methodology but may be limited by low plasma cell numbers in dilute BM or during MM remission. QPCR and FC appear most sensitive and specific for presence of t[4;14] and FGFR3 protein expression respectively. Surprisingly (given prior evidence of loss of FGFR3 expression in 25% of pts) 92% of t[4;14] pts in this series expressed FGFR3 protein. # Analyzed % Positive Sensitivity Specificity FISH 60 13 - - QPCR 65 14 87.5 97.6 Flow 82 15 85.7 91 ICC 85 15 62.5 92