Carpten, John D.

Abstract Hyperdiploid MM (H-MM), characterized by recurrent trisomies constitute about 50% of MM, yet very little is known about its pathogenesis and oncogenic mechanisms. Studies in leukemia and solid tumors have shown gene dosage effect of aneuploidy on gene expression. To determine the possible gene dosage effect and deregulated cellular program in H-MM we undertook a gene expression study of CD138-enriched plasma-cell RNA from 53 hyperdiploid and 37 non-hyperdiploid MM (NH-MM) patients using the Affymetrix U133A chip (Affymetrix, Santa Clara, CA). Gene expression data was analyzed using GeneSpring 7 (Agilent Technologies, Palo Alto, CA). Genes differentially expressed between H-MM and NH-MM were obtained by t-test (p<0.01). The majority of the differentially expressed genes (57%) were under-expressed in H-MM. Genes located on the commonly trisomic chromosomes were mostly (but not always) over-expressed in H-MM and constitute 76% of over-expressed genes. Chromosome 1 contained the most differentially expressed genes (17%) followed by chromosome 12 (9%), and 19 (8%). To examine the relationship of gene copy number to gene expression, we examined the expression of genes on chromosomes 9 and 15 in subjects with 2 copies (15 normal control and 20 NH-MM) and 3 copies (12 H-MM) of each chromosome as detected by interphase FISH. We then derived a ratio of the mean expression of each gene on these chromosomes between patients with 3 copies and 2 copies of the chromosome. If a simple relationship exists between gene expression and gene copy number, one would expect the ratio of expression of most genes on these two chromosomes to be about 1.5 in H-MM compared to NH-MM. However, many genes have ratios either higher than 2 or lower than 0.5. Furthermore, when the heterogeneity of cells with underlying trisomies is taken into consideration by correcting the ratio for the number of cells with trisomies, the actual ratio is always lower than the expected ratio. When the expression of genes on a chromosome was compressed to a median value, this value was always higher in the trisomic chromosomes for H-MM compared to NH-MM. The data suggests that although gene dosage influence gene expression, the relationship is complex and some genes are more gene dosage dependent than others. Amongst the differentially expressed genes with known function, 33% are involved in mRNA translation/protein synthesis. Of note, 37 of the top 100 differentially expressed genes are involved in these processes. In particular, 60 ribosomal protein (RP) genes are significantly (p<0.05) upregulated in H-MM. This signature in H-MM is not associated with increase proliferation as measured by PCLI. This predominant signature suggests that deregulated protein synthesis may be important for the biology of H-MM. Many of these RPs are involved in the synthesis of product of oncogenic pathways (e.g. MYC, NF-KB pathways) and may mediate the growth and survival of tumor cells. It is therefore possible that these tumor cells may be sensitive to the disruption of mRNA translation/protein synthesis. Targeting the mTOR pathway with rapamycin may therefore be useful for treatment of H-MM.

Abstract Activation of NFkB has been noted in late B-cell malignancies, but no genetic basis for this activation has been shown except for rare mutations of the NFkB pathway in lymphoma and none have been described in MM. Bortezomib, highly tumoricidal in a subset of MM patients, inhibits NFkB suggesting it’s role as a therapeutic target. Here we show for the first time the critical importance of mutations that dysregulate this potential survival pathway in human myeloma cell lines (HMCL) and new diagnosis MM patients. We performed high-density oligonucleotide array CGH (Agilent 44k) on CD138+ selected MM cells from 68 patients and 42 unique HMCL. Initially we focused on small loci, bi-allelically deleted, as markers for inactivation of potential tumor suppressor genes. We next developed FISH probes for the identified loci to confirm the bi-allelic deletions seen by aCGH. We next sequenced all samples with monoallelic deletions, and additional unselected patients, and identified additional inactivating mutations. Multiple novel tumor suppressor genes (TSG) were identified and validated, whose loss of function (both in patients and HMCL) leads to activation of the non-canonical NFkB pathway including: TRAF2 (9q34), TRAF3 (14q32), BIRC2/BIRC3 (11q22) and CYLD (16q12). Functional studies showed that HMCL with deletion or mutations in these genes have markedly increased NFkB2 p52/p100 ratios relative to other HMCL (and for BIRC2/BIRC3 relative to an isogenic line that retains one copy of BIRC2/BIRC3), indicating genetic activation of the non-canonical NFkB pathway. Reintroduction of deleted TSG into HMCL inhibited cell growth and resulted in cell death (see data on abstract by J. Keats et al). We next focused our analysis (of combined aCGH and GEP data) on positive regulators of this pathway. Rare IgH translocations of NIK have recently been identified in MM. By aCGH we identified amplification and rearrangements of NIK. We identified dysregulated expression of TNF family receptors that activate the non-canonical pathway: CD40 (translocation), TACI (amplification) and LTBR. All of the HMCL with these mutations also showed increased NFkB2 p52/p100 ratios, consistent with activation of the non-canonical NFkB. These mutations correlate with a gene expression profile of NFkB activation that is detected in one half of patients. In summary, we have determined for the first time the genetic basis for constitutive NFkB activation - present in 19/42 HMCL, and we estimate in at least one quarter of newly diagnosed patients (approximately one half of those with evidence of NFkB activation by GEP). We propose a mechanism in which positive (TACI, CD40, LTBR) and negative regulators (TRAF2, TRAF3, BIRC2/BIRC3, CYLD) of the pathway converge on NIK to mediate the processing of NFKB2 p100 to p52. The preponderance of genetic evidence suggests that drugs that target the non-canonical NFkB pathway, will be most effective in treating the 50% of MM patients with NFkB activation.

Abstract Introduction: The analysis of what goes awry in a malignant cell has focused primarily on mutations of protein-encoding genes and their regulatory sequences. However, recent work on microRNA (miRNA) has shed light on the possible involvement of miRNA genes in human disease. miRNA are small “noncoding” or “non-messenger” RNAs of about 21–25 nucleotides in length that function as regulators of gene expression essentially by pairing to the mRNA of protein-coding genes to initiate mRNA degradation or repression of translation. We are interested in characterizing miRNA expression profiles in human myeloma cell lines (HMCLs) and myeloma patient samples. Multiple myeloma (MM) is a plasma cell tumor characterized by frequent chromosomal translocations. Materials and methods: The expression of miRNA in HMCLs, primary MM cells (hyperdiploid samples) and normal plasma cells was determined using human microRNA chips. The GenoExporerTM Human microRNA chips were developed by GenoSensor (Tempe, AZ) and the microarray contains 226 human microRNA sequences in addition to control sequences. The DNA oligo probes are synthesized and immobilized on the chips (1”x 3” standard glass microslide). The probes are designed based on the active mature miRNA sequences and some of their flanking sequences. Our analysis included seven myeloma patients (hyperdiploid MM), eight different HMCLs (MM1, 8226 with t(14;16); SKMM2, U266, INA-6 with t(11;14) and H929, KMS11, UTMC-2 with t(4;14)), and eight normal human plasma cells. The normal human plasma cells were collected at Mayo Clinic Rochester from bone samples of patients undergoing orthopedic surgery. For the assay 5–10 μg of total RNA per sample were used at a concentration of 1 μg/μL. The RNA is directly labeled with biotin and used as a target for the on-chip hybridization assays. A streptavidin-Alexa dye is used to stain the hybridized targets and the fluorescent signals are captured and analyzed. The gene signal intensities were normalized to tRNA signal intensity. Results: The primary MM cells and HMCLs displayed a distinctive miRNA expression profile compared with normal plasma cells. Using the GeneSpring 7 (Agilent Technologies, Palo Alto, CA) for gene expression analysis we have identified miRNA genes with significant variation in expression levels between tumor and normal samples including miR-125b, miR-133a, miR1, and miR-124a (p<0.01). Recently defined algorithms were used to identify putative targets for the miRNA genes of interest such as leukemia inhibitory factor (LIF) as targets of miR-125b and Stat3 and angiopoietin-1 precursor as targets of miR-124a (Lewis et al. 2003). Of note, miR-15 and miR-16, previously identified to be downregulated in CLL, were expressed at low levels in some MM patients and HMCLs, but not in the normal plasma cells. Further analysis will be carried out to validate the data from miRNA profiling using northern blot analysis or real-time RT-PCR to measure expression levels of miRNA genes and that of their target genes. Conclusion: Analysis of miRNA expression pattern (along with aCGH and gene expression profiling studies) will undoubtedly refine our understanding of the various genotypic subtypes of multiple myeloma.

Abstract In an effort to identify novel genetic abnormalities in multiple myeloma (MM) we analyzed an initial set, as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative, of 94 MM patient samples on high-resolution array-based comparative genomic hybridization (aCGH) and gene expression profiling (GEP) technologies. The MMRC Genomics Initiative is a three-year research program spanning a full-spectrum of genomic technologies with the goal of analyzing 250 MM patient samples by 2009. Raw data from the Initiative are released in near real time through the MMRC Multiple Myeloma Genomics Portal. This initial aCGH dataset from the Agilent 244K platform represents the highest resolution gene copy number analysis with corresponding GEP released to date in MM. It has confirmed the cytogenetic definitions of MM with the data set being split between patients with hyperdiploid and non-hyperdiploid MM. Furthermore, the 244K platform has helped to refine minimal regions of change present on 1p, 1q, 6q, 8p, 13q, 14q, and 16q. A comprehensive screen of the dataset for genes residing in regions of homozygous deletion identified 105 genes. The most commonly affected gene was CDKN2C(p18) (8.3%) while 12 other regions were recurrently targeted. Several of these regions were recently implicated in the pathogenesis of MM but the large majority represents novel observations. The aCGH and GEP data were integrated using two different programs developed by the core institutions (The Broad Institute of Harvard & MIT and Translational Genomics Research Institute). First, Genomic Identification of Significant Targets in Cancer (GISTIC), which identifies highly significant regions of change and a limited set of candidate genes within these regions, was used to identify 285 genes associated with DNA content alterations. Second, Breakpoint Expression Correlation (BEC), which identifies dysregulated genes on either side of a copy number alteration, was used to identify 194 genes with expression changes associated with a breakpoint. This latter gene list includes all known IgH translocation target genes identified to date in myeloma. Overlapping the gene lists generated by GISTIC, BEC, and the homozygous deletion screen identified a number of genes previously implicated in MM such as CDKN2C(p18), CYLD, BIRC2/BIRC3, and two novel genes, FAM46C and RNF6. Ongoing data integration with RNAi and high content sequencing projects of the current Initiative holds great promise in furthering our understanding of MM. The MMRC Genomics Initiative and resulting efforts from the myeloma research community will increase our understanding of MM at the molecular level, help to identify new targets, and ultimately lead to the development of better, more effective therapies for myeloma.

Abstract Table 1. Samples to p53 deletions p 53 deletion FISH aCGH FISH and aCGH MGUS 142 - - SMM 108 - - Newly diagnosed MM - 182 - Relapsed/refractory MM 62 156 19 HMCLs - 48 - Total 312 386 19 Background: Inactivation of p53 by mutation or allelic loss is a rare event in multiple myeloma (MM) at the time of disease diagnosis and believed to be more common in the late stages of the disease. Here we defined the prevalence of p53 deletions in monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), newly diagnosed MM, relapsed MM and human myeloma cell lines (HMCLs). Indirect mechanisms of p53 inactivation such as amplification of MDM2, or deletion of CDKN2A (p14ARF) were also investigated. Patients and Methods: A total of 631 MM samples and 48 HMCLs were analyzed for p53 abnormalities (Table 1) and compared with a cohort of plasma cell leukemia (PCL) previously published by us. Interphase fluorescence in situ hybridization (FISH) and highresolution array-based comparative genomic hybridization (aCGH) were used to detect p53 deletions. MDM2 amplification and p14ARF loss were evaluated by aCGH. Overall survival (OS) was estimated through Kaplan Meier method. Results: The prevalence of p53 deletions was 1.4%, 3.7%, 8.9%, 20% and 87.5% in MGUS, SMM, newly diagnosed MM, relapsed MM and HMCLs respectively (Figure 1). Of interest patients in first relapse showed a p53 deletion prevalence of 18% in comparison with 33% for patients in second relapse or more. We obtained p53 deletion status in 8 patients noted to have deletion at the time of relapse and in whom we had previous samples stored. In 7 of 8 cases deletions had been acquired (absent in the previous samples). The median OS for relapsed MM patients with or without p53 deletion were 4.2 months and 37.8 months respectively (p <0.001). MDM 2 amplification and p14ARF loss was detected in 0.4% and 2% of new diagnosed MM, 0.6% and 5.1% of relapsed MM and 2% and 29% of HMCLs respectively. Discussion: Emergence of p53 deletion/mutations denotes progression genetic events in MM. Importantly we show for the first time the overriding importance of p53 deletions as prognostic markers in relapsed MM. A molecular staging system that is based on the presence of p53 inactivation may be more powerful than classifying patients based on the loss of p53. Other abnormalities involving p53 pathway regulators like MDM2 gain and p14ARF loss are infrequent events in MM, even in advanced disease. Figure 1. Prevalence of p53 deletions Figure 1. Prevalence of p53 deletions

Abstract The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative was instigated to harness the power of multiple genomic approaches to further the understanding of multiple myeloma. To date, 137 samples from patients with newly diagnosed and relapsed myeloma (out of an estimated final total of 250 by the end of the year) have been subjected to expression profiling and array comparative genomic hybridization. To identify regions of recurrent copy number alteration with a high degree of confidence, we have used the Genomic Identification of Significant Targets In Cancer (GISTIC) algorithm, which detects such regions and assigns a probability to each. Application of GISTIC to the MMRC collection identified 14 significant regions of amplification and 15 significant regions of deletion. The algorithm further detects peaks of copy number change that contribute to each region’s significance. Of genes that were expressed in our dataset, a total of 64 across the genome were found to lie within the boundaries of significant amplification peaks and 30 were found to lie within significant deletion peaks. Given the likelihood that this gene collection is highly enriched for genes important in the pathogenesis of myeloma and potential therapeutic targets, it has been prioritized for further validation with the amplified genes submitted for arrayed RNAi and the deleted genes expedited for re-sequencing. In order to define a poor prognosis group, we developed and applied a model based on the gene expression dataset of Shaughnessy and colleagues. To identify pathways activated in poor prognosis disease, we applied Gene Set Enrichment Analysis with the Molecular Signatures Database. This demonstrated enrichment of multiple canonical pathways within the poor prognosis group, including those associated with proliferation, cell cycle progression and DNA repair. A search for recurrent copy number events associated with poor prognosis disease revealed that by far the most significant event, and the only one surviving correction for multiple hypothesis testing, was mono- or bi-allelic deletion of CDKN2C (p18). Interestingly, the poor prognosis group also demonstrated higher expression of p18 in those samples without bi-allelic deletion and of CDKN2A (p16). Deletion and increased expression of p18 in high proliferation index myeloma samples has previously been described. However, our analyses demonstrate the pre-eminence of p18 loss for defining poor prognosis disease compared to other recurrent copy number changes. Furthermore, it expands on work in other tumors demonstrating feedback mechanisms between cyclin dependent kinase inhibitor pathways, suggesting a more complex model of interplay between the pathways than previously described. In conclusion, the MMRC reference collection is proving an important tool in the fight to understand key genetic events in multiple myeloma and would ultimately be anticipated to contribute towards development of improved therapy for the disease.

Abstract The retinoblastoma 1 (RB1) gene is a frequent target of mutation or inactivation in neoplastic diseases. However, the current model for RB1-mediated malignancy requires inactivation of both alleles for tumor progression. Single–copy loss of RB1 has been shown to correlate with an approximate 50% reduction in RB1 mRNA expression in patients diagnosed with multiple myeloma and survival in this cohort of patients is significantly reduced. Using siRNA to reduce Rb protein levels to 50% and Rb-expressing adenovirus to restore RB1, variations in proliferation and cell cycle were observed. The KMS–11 adherent cell line with normal Rb protein expression was labeled with carboxyfluorescein succinimidyl ester (CFSE) for tracking of cellular division and RB1 siRNA added to silence 50% of protein expression. Transfection efficiency for siRNA was measured at 95% or greater. Flow cytometric analysis was performed at 72 hours to identify changes in proliferation between controls and Rb knock–down. Using CFSE proliferation analysis software, reduction of Rb protein levels by 50% caused an average increase in the proliferation index from 2.05 to 2.50. Additionally, reduction of Rb protein levels caused an increase in the percentage of cells in S–phase, from 24 to 29%. CFSE and cell cycle experiments were performed in triplicate. Conversely, addition of Rb–expressing adenovirus to MM.1R and U266 cell lines, with mono-allelic and bi-allelic loss of RB1 respectively, resulted in a decrease in proliferation assayed by CFSE and an average decrease in the percentage of cells in S-phase from 29 to 23% compared to controls. No changes in proliferation or cell cycle compared to untreated controls were observed following null adenovirus infection. Western blot and quantitative RT–PCR (qPCR) were used to confirm reduction or addition of RB1 to cell lines. Further, qPCR was used to identify potential activation of the interferon response following addition of siRNA and adenovirus. No changes in annexin V expression were observed following reduction or replacement of Rb as compared to controls. These results suggest the addition of proliferation advantages to tumors with single copy loss of RB1. For malignancies such as multiple myeloma characterized by deletion of 13q14 coupled with an extended period of development, the proliferation advantage associated with RB1 haploinsufficiency may contribute to decreased survival in the deletion 13 cohort.

Abstract Constitutive activation of NF-κB signaling pathways has been identified in a variety of malignancies, however the molecular basis has remained largely unknown. Recently, we have shown mutations in 10 genes encoding positive and negative regulators of NF-κB signaling pathways in 20% of multiple myeloma (MM) patients, with TRAF3 being the most commonly affected gene. TRAF3 is a negative regulator of NF-κB signal and its inactivation results in the constitutive activation of the alternative or noncanonical NF-κB signaling pathway. With the exception of two recent studies in MM, there is no available data regarding the mechanisms that dysregulate NF-κB in other B-cell malignancies. The aim of this study was to analyze the TRAF3 status in a cohort of Waldenström’s macroglobulinemia (WM) patients. Forty-nine WM were analyzed by using a combination of array-based comparative genomic hybridization (aCGH) and cIgM-FISH. Genome sequencing from 28 of these patients was performed on all coding exons of TRAF3 and the adjacent intron-exon junctions. A NF-κB index, as a measure of NF-κB transcription activity, was calculated from 21 patients with gene expression data. The index is the mean expression level of four probe sets differentially expressed between human myeloma cell lines with identified mutations compared to those without identified NF-κB pathway mutations (CD74, IL2RG and 2x TNFAIP3). By performing aCGH we identified a small monoallelic deletion (∼460 Kb), including the TRAF3 locus in one of 22 patients. Genome sequencing identified a missense mutation (D483N) in the remaining allele. This substitution affects a highly conserved amino acid throughout vertebrate evolution, suggesting its functional relevance. We used cIgM-FISH to screen 27 patients with no available aCGH data and we found one biallelic TRAF3 deletion and one chromosome 14 monosomy. In the later case, no DNA was available to sequence the remaining allele. Both samples with biallelic inactivation of TRAF3 possess the two highest NFKB index values of our WM cohort, suggesting NF-κB transcription activity. Moreover, the case with biallelic deletion showed the lowest level of TRAF3 gene expression. Additionally, the entire TRAF3 coding region was sequenced in 28 of these 49 patients and one homozygous nonsense mutation (K286X) was identified. No GEP was available in this sample to calculate the NF-κB index. TRAF3 was only recently implicated in a malignant process, when inactivating mutations and biallelic deletions were reported in approximately 13% of MM patients, making it the most frequently inactivated tumor suppressor in MM. Our study has identified biallelic TRAF3 inactivation in at least 3 of 49 WM patients, suggesting that the constitutive activation of NF-κB signaling pathways may be involved in the pathogenesis of WM. Recent findings showing an association between TRAF3 loss of function and good response to bortezomib in MM patients suggest that proteasome inhibitor-based therapy may be an attractive option in the treatment of the WM patients with constitutive activation of NF-κB signaling pathways. Additional mutational screening of TRAF3 and other regulators of these pathways are ongoing to determine the extent of constitutive activation present in WM and other B-cell malignancies.

Abstract The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative is a three-year program to analyze tumor tissue from hundreds of multiple myeloma (MM) patients via gene expression profiling (GEP), comparative genomic hybridization (aCGH), and exon re-sequencing. In addition, RNAi knockdown of selected genes in MM tumor cell lines is being evaluated to identify potential new targets. All genomic data generated is scheduled for placement in an open-access Multiple Myeloma Genomics Portal pre-publication and in near real-time (www.broad.mit.edu/mmgp). Additionally, samples are also destined for drug validation and correlative science on clinical protocols as this study moves forward. This comprehensive project is spearheaded by the MMRC and conducted via collaboration with the Eli and Edythe L. Broad Institute of MIT and Harvard, the Translational Genomics Research Institute (TGen), Mayo Clinic Arizona, and The Dana-Farber Cancer Center. The study is supported by the collection from member institutions of the MMRC of bone marrow aspirates and matched peripheral blood samples from over 1000 patients. Specific genomic technologies that are currently being employed across this sample set include GEP using Affymetrix Human Genome U133A 2.0 Plus Arrays, and, in parallel, efforts to identify regions of genomic gain and loss are using Agilent Human Genome CGH arrays. In contrast to other large-scale genomic projects based on exon-sequencing of targeted gene sets, this project will be the first to perform genome-scale single molecule sequencing (SMS) of DNA from patient specimens. Results will be targeted against candidate classes of genes (e.g. kinases, phosphatases, known oncogenes and tumor suppressors), and genes from GEP or within candidate regions of copy gain or loss identified by the aCGH experiments. Mutations will be further validated in an independent set of patient specimens. Finally we will attempt to identify points of vulnerability of MM through systematic loss-of-function screens in myeloma cell lines using high-throughput RNA interference (using both shRNA and siRNA platforms). Importantly, data generated from this genomics initiative will ultimately be made public pre-publication through the established MMRC Multiple Myeloma Genomics Portal. Data from all aspects of this project (sample collection and analyte isolation, GEP, aCGH, SMS, RNAi and bioinformatics) will be described in this presentation. The power of this study is the comprehensive collection of gene expression, CGH, and genome sequencing on a single reference set of clinically annotated samples. The addition of RNAi screens makes this a very important and unique data resource, which we hope will help expedite the discovery of novel targeted agents for MM scientific community.

Abstract Genetic abnormalities are important in the pathobiology of myeloma and established prognostic factors. High-density aCGH allow global unbiased detection of genomic gains and losses. In this study, we attempt to identify survival critical genomic aberrations by correlating aCGH-defined abnormalities with survival in 2 different cohorts of patients. aCGH was performed in 64 MM patients treated with high dose therapy from the Mayo Clinic using Agilent’s 44k DNA microarray genechip. In addition, a publicly available aCGH dataset performed in 67 MM patients treated with total therapy from the University of Arkansas Medical School using the lower density Agilent 22k chip (Human 1A v2) was also analyzed. Base 2 logarithms of the ratios between red and green intensities (log-ratios) were treated as a primary measure of copy number ratio between tumor and normal samples. To detect the regions of genomic aberration, values of a sliding window of 15 consecutive log-ratios were compared with those of an artificial ‘reference’ chromosome using the Wilcoxon rank sum test. The reference chromosome, constructed for each sample, was assembled by collecting log-ratio values close to zero. For this, 15 randomly sampled log-ratios from autosomal regions whose p-value of Wilcoxon signed rank test against 0 was larger than 0.05 were recursively accepted until the number of log-ratios in the assembled reference chromosome reached 1000. The p-values and their corresponding false discovery rates (q-values) for the copy number gain or loss were calculated independently for each sliding window and assigned to the center position of corresponding sliding window. The survival difference was examined between samples that had gained or lost a chromosomal region (sliding window) and samples that had not using the method of Kaplan-Meier and the statistical significance was assessed by the log-rank test. The sliding window was identified as gained or lost if P-value < 10 −4 and Q-value < 10[suo]-3. Sliding windows whose gains or losses led to poor survival with log-rank test p-value < 0.5, corresponding q-value < 0.5, and happened to ≥ 10% of all samples were retained. Several regions of genomic gains and losses were significantly associated with poorer survival with 5 deletion hotspots common between the 2 datasets (Table). We validated the prognostic significance of these survival critical loci using FISH in an independent dataset of 169 patients treated with high dose therapy. Clustering of these survival critical loci revealed clusters of MM characterized by low, intermediate or high number of these abnormalities, which are independent of ploidy and translocations subtypes. These subgroups have significantly different survival. In addition, the clustering analysis suggests the chromosome 13 loss is an early event whereas chromosome 1 abnormalities are late events. Our strategy therefore identified novel regions of genomic aberration that are important prognostically. However, it is likely that these regions are manifestation of genomically unstable tumors with complex abnormalities that have poorer prognosis. Chr Start End Length Cytoband 1 74,137,824 75,972,544 1,834,720 p31.1 1 62,433,123 84,792,809 22,359,686 p31.3 – p22.3 20 4,100,452 6,189,410 2,088,958 p13 – p12.3 20 6,717,170 13,142,205 6,425,035 p12.3 – p12.1 20 4,708,668 10,566,333 5,857,665 p13 – p12.2