TC Classification Research Articles

Oral squamous cell carcinoma - do we always need elective neck dissection? evaluation of clinicopathological factors of greatest prognostic significance: a cross-sectional observational study.

With regard to TPS, FOM appeared to be the most metastatic. However, the recurrence rate was similar to TC tumors, which were characterized by higher G than those in other locations. When analyzing G, the highest percentage of LR (40.5%) and NM (34.5%) was observed among patients with G2. As G increased, so did the number of pENE G1 - 7.4%; G2 - 31%; G3 - 35.7%; LVI: G1 - 25.9%; G2 - 50%; G3 - 57.1%; PNI: G1 - 29.6%; G2 - 47.6%; G3 - 92.9%; NR G1 - 14.8%; G2 - 32.1%; G3 - 21.4%. Grading did not affect the type of growth and did not directly affect the occurrence of NR. pT and DOI increased the frequency of NM but we did not observe any effect of pT and DOI on LR, PNI, and LVI. NY in the study group did not increase the risk of NR. Tumor primary sites within the FOM, TC, and pT classification are the factors that increase the risk of NM and LR. However, apart from the primary site predisposing to the occurrence of NM, the histological structure of the tumor turned out to be the most important feature affecting the patient's prognosis. The number of cases of pENE+, LVI+, PNI+, NM+, and NR+ increased with the increase in G. Although the pT, DOI increased the frequency of NM, we did not observe the effect of the pT and DOI on LR, PNI and LVI. Thus, even in the case of a small tumor of the FOM and TC with at least G2, elective neck dissection should be performed each time.

A novel optimal power management strategy for plug-in hybrid electric vehicle with improved adaptability to traffic conditions

Adaptability to various driving conditions (TCs) is one of the essential indicators to assess the optimality of power management strategies (PMSs) of plug-in hybrid electric vehicles (PHEVs). In this study, a novel optimal PMS with the improved adaptability to TCs is proposed for PHEVs to achieve the energy-efficient control in momentary scenarios by virtue of advanced internet of vehicles (IoVs), thus contributing to remarkable promotion in fuel economy of PHEV. Firstly, the optimal control rules in the novel PMS, corresponding to diverse driving conditions, are optimized offline by the chaotic particle swarm optimization with sequential quadratic programming (CPSO-SQP), which can effectively endow the global optimization knowledge into the rule inspired method. Then, an online TC identification (TCI) method is designed by cooperatively exploiting multi-dimensional Gaussian distribution (MGD) and random forest (RF), where the MGD based analysis on the macrocosmic state of traffic contributes to valuable inputs for the RF based TC classification, and additionally the super regression ability of RF further improves the identification accuracy. Finally, the numerical simulation validations showcase that the novel optimal PMS can reasonably and instantly manage the power flow within power sources of PHEV under different TCs, manifesting its anticipated preferable controlling performance.

Influence of Predictor Genes of TC Classification on Clinical Outcome in Multiple Myeloma

Influence of Predictor Genes of TC Classification on Clinical Outcome in Multiple Myeloma

The Mutational and Signaling Landscape of Multiple Myeloma Varies Dependent upon Translocation Cyclin D (TC) Subgroup

IntroductionThe spectrum and frequency of mutations in newly diagnosed and relapsed MM has been reported and exhibits a pattern which is distinct from other peripheral lymphoid disorders. A number of data sources have suggested that MM is not a single disease, but rather a collection of molecularly diverse entities which present as malignancies of plasma cells with different clinical courses and response to therapy. To test this hypothesis we examined the spectrum of common mutations and their associations with gene expression in etiological subgroups defined by a simplified TC classification based on translocation subgroup and the deregulation of a D group cyclin.Materials and methodsWe examined a set of gene expression data from 907 newly diagnosed MM patients and classified them according to an updated TC model. An additional set of 482 cases underwent both sequencing by the FoundationOne Heme targeted panel and gene expression profiling. Mutational data were analyzed for significant associations with gene expression data within five primary TC subgroups: D1, D2, CCND (translocated 11q13 or 6p21), MMSET, and MAF.ResultsGene expression signatures of GEP70 risk status, chromosomal aberrations [1q+, 1p-, 13q-, 17p-, HRD], proliferation index, NF-kB activation, and BCL2/MCL1 ratio were not distributed evenly across the TC subgroups, consistent with distinct biological differences amongst groups. Having noted differential expression patterns between subgroups, we hypothesized that distinct patterns of mutation may also exist across TC subgroups.Mutations in RB1 and CDKN2C, seen primarily in the D2 and MMSET subgroups, were mutually exclusive yet both associated with increased proliferation and HR status. Thus two paths to extreme proliferation emerge through either ahomozygous deletion of CDKN2C (with low expression of CDKN2C) or an RB1 alteration (with high expression of CDKN2C).Mutations in KRAS, NRAS, and BRAF were not evenly distributed across subgroups and were significantly inversely associated with an NF-kB signature. RRAS2 was also significantly inversely associated with MAPK mutations in the D1 and D2 subgroups, while genes encoding sprouty-related proteins, SPRED1 and SPRED2, were positively associated with MAPK mutations in the CCND-11q13 subgroup.Overall, MAPK mutations were most significantly associated with elevated expression of DKK1, a known Wnt antagonist, and DUSP6, a known inhibitor of the pathway. These gene expression patterns were primarily localized in the D1, D2, and CCND-11q13 subgroups. In contrast, the MMSET and MAF subgroups had a unique patterns of expression not seen in the D1, D2, or CCND subgroups.A gene set enrichment analysis showed that the DNA replication and cell cycle pathways were significantly enriched in the MMSET and MAF subgroups in the presence of MAPK pathway mutations. Within the MMSET subgroup, MAPK mutations were positively associated with GEP70 HR, proliferation index, and membership of the UAMS-PR subtype, while being negatively associated with FGFR3 expression. This result indicates that the reliance on FGFR3 signaling as an oncogenic driver is lost in the presence of a MAPK activating mutation.ConclusionsIn MM proliferative signals are delivered via the RAS and NF-kB pathways and activation of these two pathways appears to offer mutually exclusive pathways to disease progression since the majority of cases exhibit a reciprocal relationship between these two signaling pathways.The MMSET and MAF subgroups lack a strong association between NF-kB and MAPK signaling pathways, which may indicate that their initiating translocation event and subsequent genetic patterns provide a unique background in which MAPK alterations accelerate progression.As mutational interactions are differential across TC subgroups, we propose a comprehensive approach to MM classification that includes the etiologic designation by simplified TC subgroups and the subsequent use of genetic and biological markers characteristic of acquired features associated with disease progression, such as MAPK or NF-kB activation.Distinct patterns of RNA expression are associated with DNA mutations in myeloma when contextualized by etiologic subgroups. This indicates that the cellular background in which a mutation occurs has a distinct impact on downstream expression patterns-this observation is particularly relevant to the MMSET and MAF subgroups. DisclosuresPawlyn:Celgene: Consultancy, Honoraria, Other: Travel Support; Takeda Oncology: Consultancy. Morgan:Univ of AR for Medical Sciences: Employment; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Bristol Meyers: Consultancy, Honoraria; Janssen: Research Funding.

Molecular Subtyping and Risk Stratification for the Classification of Myeloma

Molecular Subtyping and Risk Stratification for the Classification of Myeloma

Molecular Subgroups of Hyperdiploidy and Their Prognostic Relevance - an Analysis of 1,036 Myeloma Trial Patients

Introduction Hyperdiploidy (HRD) comprises the largest pathogenetic subgroup of myeloma. However, its clinical and molecular characterisation is incomplete. Here, we investigate HRD using a novel high-throughput molecular analysis method (MyMaP - Myeloma MLPA and translocation PCR; Kaiser MF et al., Leukemia 2013; Boyle EM et al., Gen Chrom Canc 2015) in a large cohort of 1,036 patients from the UK NCRI Myeloma XI trial. Materials, Methods and Patients Copy number changes, including gain of chromosomes 5, 9 and 15, as well as translocation status were assayed for 1,036 patients enrolled in the UK NCRI Myeloma XI (NCT01554852) trial using CD138+ selected bone marrow myeloma cells taken at diagnosis. HRD was defined by triploidy of at least 2 of analysed chromosomes 5, 9 or 15. Analysis was performed on standard laboratory equipment with MyMaP, a combination of TC-classification based multiplex qRT-PCR and multiplex ligation-dependent probe amplification (MLPA; MRC Holland). The parallel assessment of multiple loci with copy number alteration (CNA) by MLPA allowed unbiased association studies using a Bayesian approach. Semi-quantitative gene expression data for CCND1 and CCND2 was generated as part of the multiplexed qRT-PCR analysis. Median follow up for the analysis was 24 months. Results Of the 1,036 analysed patients, 475 (46%) were HRD. Of these, 325 (68%) had gain(11q25), 141 (29.7%) gain(1q), 43 (9.1%) del(1p32) and 36 (7.5%) del(17p). Gain(11q25) was significantly associated with HRD (Bayes Factor BF 01 HRD cases were classified as D1, D2 or D1+D2 according to the TC classification based on qPCR CCND1 and CCND2 expression values and expression was correlated with copy number status. An association of the D1 subtype with gain(11q25) and of D2 with gain(1q) was confirmed. CCND1 expression was significantly ( P CCND2 expression values were significantly higher in cases with gain(1q) [Mean RQ 8,723] than in cases with gain(11q) [mean RQ 1,087] ( P HRD was associated with favourable outcome when compared to non-HRD cases with median PFS 28.8 vs. 21.7 months ( P Within HRD cases, gain(1q) was associated with shorter PFS ( P =0.02) and OS ( P =0.009), associating the D2 group with inferior outcome. Presence of del(1p32) was associated with inferior PFS ( P =0.01) and OS ( P =0.0007) in the HRD subgroup and del(17p) was associated with inferior OS ( P =0.04) with a trend for PFS. HRD cases with presence of any of the risk factors gain(1q), del(1p32) or del(17p) in comparison to those without had a median PFS of 25.1 vs 35.1 months ( P =0.0001) and 24-month OS of 73.8% vs 89.0% ( P C onclusion We describe in a large trial cohort an association between gain(11q25) and the D1 hyperdiploid subtype as well as gain(1q) and the D2 subtype, a finding that has so far only been inferred by gene expression array data in the original TC classification. We also find an association with adverse outcome for the D2/gain(1q) subtype. Our findings demonstrate that the novel molecular approach MyMaP allows precise molecular sub-classification of HRD myeloma. Disclosures Kaiser: BristolMyerSquibb: Consultancy; Chugai: Consultancy; Janssen: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Pawlyn: Celgene: Honoraria, Other: Travel support; The Institute of Cancer Research: Employment. Jones: Celgene: Other: Travel support, Research Funding. Savola: MRC Holland: Employment. Owen: Celgene: Honoraria, Research Funding; Janssen: Honoraria. Cook: Takeda Oncology: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Speakers Bureau; Sanofi: Consultancy, Speakers Bureau; BMS: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau. Gregory: Janssen: Honoraria; Celgene: Honoraria. Davies: Onyx-Amgen: Honoraria; Celgene: Honoraria; University of Arkansas for Medical Sciences: Employment; Takeda-Milenium: Honoraria. Jackson: Amgen: Honoraria; Takeda: Honoraria; Celgene: Honoraria. Morgan: Celgene: Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding; Weisman Institute: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity9s Board of Directors or advisory committees; Takeda-Millennium: Honoraria, Membership on an entity9s Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; CancerNet: Honoraria; MMRF: Honoraria.

Pretransplant induction with VTD (Bortezomib/Thalidomide/Dexamethasone) significantly improves PFS: long-term results of the randomized phase 3 PETHEMA/GEM study

Kingdom; Department of Hematopathology, Aalborg University Hospital, Denmark; Clinical Cancer Research Center, Aalborg University Hospital, Denmark and The Department of Clinical Medicine, Aalborg University, Denmark Background: Today’s diagnostic tests for multiple myeloma (MM) reflect the criteria of the updatedWHO classification based on biomarkers and clinicopathologic heterogeneity. To that end, we propose a new biological subtyping of myeloma plasma cells (PC) by B-cell subset associated gene signatures (BAGS), from the normal Bcell hierarchy in the bone marrow (BM). Here we document the prognostic and biological value of subtyping, as shown for DLBCL (JCO 2015 Apr 20;33:1379). Patients and Methods: We combined FACS and GEP to generate BAGS classifiers for the PreB-I, PreB-II, immature, naive, memory (M) and PC subsets in normal BM. Construction was based onmedian-centered probe sets from the BM data using regularized multinomial regression with six discrete outcomes representing BAGS, by a total of 55 genes varying from 1524 per subtype. Each patient underwent BAGS assignment according to the highest predicted probability score above 0.45 or was otherwise unclassified. The impact of BAGS was analyzed using six clinical cohorts, gathered across geographical regions, time eras, and sampling methods. The analysis estimated subtype frequencies and included a prognostic meta-analysis of 926 patients treated with high dose melphalan as first line therapy in 3 prospective trials: UAMS, HOVON65/GMMG-HD4, MRC Myeloma IX data with the Affymetrix U133 plus 2.0 microarray data available from myeloma PC samples. To compensate for cohort-wise technical batch effects, each cohort was median centered and adjusted probe set-wise to have same variance as the BM data. Results: The resultant tumor assignments exhibited similar BAGS subtype frequencies across 1302 individual MM cases with identical frequencies in the six cohorts. The BAGS subtypes were significantly associated with overall and progression free survival (OS, P1⁄41.1e-16 and PFS, P1⁄47.3e-9) in the metaanalysis of 926 pts. The major impact was observed within the PreB-II and M subtypes conferred with significant inferior prognosis compared to the Im, N and PC subtypes as illustrated in Figure 2. Cox proportional hazard meta-analysis showed that the BAGS subtypes added significant and independent prognostic information to the TC classification system and ISS staging. In parallel we found significant correlation between the PreBII subtypes and the proliferation index, risk profiling (P<0.001). Conclusion: We have documented myeloma PC differences with prognostic impact in support of reversible phenotypic plasticity in MM. 2. Multiple Myeloma Therapy in Newly Diagnosed Patients including Transplantation

Gene Expression Profiling Of Multiple Myeloma Samples Using RNA Sequencing Identifies Frequent Rearrangements Of FCHSD2

The Multiple Myeloma Research Consortium (MMRC) has characterized over 300 hundred patient samples using a variety of platforms as part of the Multiple Myeloma Genomics Initiative (MMGI). Part of this large study includes a subset of 84 patients that were screened for somatic mutations using whole genome sequencing (WGS) or whole exome sequencing (WES) in combination with mRNA sequencing. This represents one of the first cohorts of myeloma patients with matched genome and transcriptome sequencing results. Given the historic value of microarray based gene expression profiling (GEP), this cohort provides the unique opportunity to compare gene expression measurements from the two platforms as Affymetrix U133Plus2.0 based GEP was performed on 42 of these samples.As part of the MMGI study, the Broad Institute has completed the genome sequencing, using WGS and WES, for 213 patients. A frequently mutated list of 9 genes including NRAS, KRAS, TP53, PNRC1, MAGED1, FAM46C, DIS3, CCND1 and ALOX12B were identified initially. Given the potential for RNAseq data to be used to define gene expression levels and to identify mutations in expressed genes we tested the feasibility of mutation calling on RNAseq alone. We independently called mutations on the entire transcriptome of the 84 patients and used a filtering method to eliminate likely germline variants in the absence of a matched normal control. We looked for point mutation concordance between, the calls identified by RNA-Seq alone and the previously reported variants through exome sequencing in the 9 frequently mutated genes. Out of the 66 SNV's identified by these criteria using WGS or WES sequencing, 55(84%) were detected using RNA-Seq. Of the remaining 11 loci, 7(10%) were not detectably expressed and in 4(6%) cases the mutation was not detectable even though there was ample coverage. It is unclear if the last 6% represent false positives in the genome calls or the preferential expression of the wild-type allele.To interrogate the utility of RNAseq based GEP in myeloma we independently recapitulated many of common GEP measurements. First we independently used the 84 samples to define cutoffs for the implementation of the TC classification method. We compared our independent assignment of the 42 samples with matched gene expression array data, to their existing microarray assignments. This resulted in 40/42 (95%) samples being classified 40(95%) into identical TC classes. The two discordant samples MMRC0312 and MMRC0387 classified as TC class “none” by expression arrays were classified as other classes by RNAseq. MMRC0312 exhibited high CCND3 expression using RNA-Seq and was assigned to ‘6p21' class. MMRC0387 exhibited elevated CCND1 expression and was classified as ‘D1' using RNAseq. For the indexes we showed a strong correlation for the proliferation index (R2=0.971) and the NFKB index (R2=0.961) but only a moderate correlation for the 70-gene index (R2=0.761). The decreased correlation in the 70-gene index is clearly due to the large number of probesets used, which are associated with genes that are clearly not expressed by RNA-seq.One additional advantage of RNAseq over microarray based gene expression measurements is the potential to detect fusion transcripts. We have applied fusion transcript detection to this cohort of patients and 69 human myeloma cell lines, which were also screened by RNAseq and WES as part of the MMGI study. The most common fusion transcript detected is the @IGH-MMSET fusion characteristic of t(4;14). The next most common fusion we identified appears to be a promoter replacement event were the highly expressed gene, FCHSD2, is fused to multiple partners including known myeloma related genes, MMSET and MYC, and previously unreported genes in myeloma, CARNS1 and NCF2. Additional structural rearrangements involving FCHSD2 are also predicted based on the high frequency of copy number abnormalities encompassing the 5′ region of this gene as detected by comparative genomic hybridization in the MMGI study.This study should provide the basis for the migration of myeloma based gene expression profiling from microarrays to RNA sequencing based approaches. In the future RNA sequencing has the potential to provide novel classification schemes that leverage the multitude of measurements that can be made from this single assay. Disclosures:Levy:MMRC: Employment.

A Rapid and Robust Molecular Diagnostic Approach For Multiple Myeloma – Results From a Large Trial Cohort

IntroductionObtaining reliable information about the molecular subtype of myeloma is and will become ever more important in a number of clinical settings, such as alternative treatment strategies for high risk or ultra high risk disease (Boyd KD et al., Leukemia 2011), or patient selection for small molecule inhibitors, that are currently under development, targeting myeloma subtype specific proteins (e.g. MMSET or MAF). We report here our experience with a novel, highly applicable and high throughput diagnostic approach in a large sample set of 1016 myeloma presentation cases, using a combination of qRT-PCR and Multiplex Ligation-dependent Probe Amplification (MLPA) for molecular patient characterization of Ig loci translocations and well-defined copy number abnormalities. Material and MethodsRecurrent translocations were assessed for 1016 presentation NCRI Myeloma XI trial cases and 41 matched relapse samples, using a previously published and interphase fluorescence in situ hybridization (iFISH)-validated in house qRT-PCR assay on purified bone marrow plasma cell material. The assay measures expression of translocation partner genes and their downstream effectors (e.g. CCND1, MMSET, FGFR3, MAF, MAFB, CCND2) with subsequent interpretation and categorization of results based on the translocation/cyclin D (TC) classification. This allows prediction of presence of the recurrent translocations with high sensitivity and specificity (Kaiser MF et al., Leukemia 2013) and evaluation of overexpression of potential drug targets independent of translocations (e.g. MAF). For selected cases, the myeloma specific SALSA MLPA assay (MRC-Holland) was performed, containing 46 probes that inform about prognostically relevant copy number alterations, such as del(1p), gain(1q), or del(17p). High correlation between MLPA and FISH results for clinically relevant copy number aberrations has been previously reported (Alpar D et al., Genes Chrom Canc 2013). ResultsThe TC classification based translocation qRT-PCR assay worked reliably even for poor quality input RNA, providing results for >96% of analyzed samples. Predicted translocation frequencies among the 1016 evaluable cases were comparable to previously reported results [t(11;14): 16.6%; t(4;14): 12.6%, of which 21.1% lacked FGFR3 expression; t(14;16): 2.6%; t(14;20): 0.5%; t(6;14): 0.7%]. Relapse samples showed consistent results with matched presentation samples, with one t(4;14) case losing initial high FGFR3 expression at relapse. Correlation with clinical data will be available for presentation at the meeting. Measurement and analysis of the samples was performed by a single lab technician in a short time, demonstrating the high throughput capability of the method. This makes rapid analysis of very large sample collections possible, revealing novel findings. When the assayed group was split by median age, the younger group (22-66 years) contained relatively more t(4;14) [15.7% vs. 9.4%; p=0.003] cases than the older group (67-88 years), consistent with recent reports on iFISH data (Avet-Loiseau H, 2013). We also found a lower frequency of t(11;14) [13.6% vs. 19%;p=0.022] in the younger vs. the older group, which has not been reported.MLPA results were generated for a subset of 30 samples for which iFISH and copy number array data were available. The previously reported high level of correlation with iFISH results (Alpar D et al., Genes Chrom Canc 2013) was confirmed and extended for copy number array data, with >85% sensitivity and >95% specificity for del(1p), gain(1q), del(13p) and del(17p). MLPA assessments will be extended in the coming months to include a large group of Myeloma XI cases, and results and their associations with qRT-PCR results and clinical features will be presented at the meeting. ConclusionPrecision medicine approaches in myeloma require fast, robust and practicable molecular diagnostic tools. The current diagnostic standard iFISH doesn't fulfill any of these criteria. Other approaches such as microarray analyses have never found acceptance outside of highly specialized centers due to practicability issues. With the approach presented here, clinically relevant molecular features can be assessed within 48 hours with standard molecular laboratory equipment. This approach is a suitable candidate for a novel standard for routine clinical molecular analysis of multiple myeloma. Disclosures:Savola:MRC-Holland: Employment.

Abstract 806: RNA sequencing based prognostic indexes show a high degree of correlation with historic array based gene expression measurements in multiple myeloma.

Abstract The Multiple Myeloma Research Foundation (MMRF) and the Multiple Myeloma Research Consortium (MMRC) have pioneered efforts towards understanding the genomic underpinnings of multiple myeloma. The MMRC centers have submitted over 1900 tumor samples to the MMRC tissue bank, from which &amp;gt;400 have been used in the Multiple Myeloma Genomics Initiative (MMGI), which is a multi-institutional comprehensive study to unveil the complete genomic blueprint of myeloma with public data access. In the initial phase 238 myeloma tumors were analyzed for somatic copy number alterations using array-based comparative genomic hybridization (aCGH) and array-based gene expression profiling (GEP). The large majority of this reference collection was also analyzed by array-based methylation profiling. We have also performed whole genome or exome sequencing on &amp;gt;200 normal/tumor pairs. These studies led to several key discoveries including global hypomethylation, homozygous deletion of UTX, novel mutations in FAM46C (13%) and DIS3/RRP44 (11%), and potentially therapeutically actionable BRAF mutations (4%). To improve our understanding of the transcriptional activities in myeloma, we have performed RNA-sequencing on 84 myeloma patient samples and 65 human myeloma cell lines for which whole genome or exome sequencing data is also available. RNA-seq has a number of advantages over GEP including improved dynamic range and the detection of exon and isoform levels, allele specific expression, and fusion transcripts. There are a number of robust gene expression classifications and indexes with prognostic value for myeloma samples tested by GEP. Within the cohort of 84 patient samples there are 42 samples with matched GEP data. Using this subgroup we tested if the current GEP methods could be directly moved to RNA-seq data by matching the current probesets to genes. We then compared the results across the patient set for each index or classification. For classifications we determined that the TC classification could be directly transitioned to RNA-seq data with 100% concordance. For the indexes we showed a strong correlation for the proliferation index (R2=0.971) and the NFKB index (R2=0.961) but only a moderate correlation for the 70-gene index (R2=0.761). The decreased correlation in the 70-gene index is clearly due to the large number of probesets used, which are associated with genes that are clearly not expressed by RNA-seq. Integration of the DNA and RNA sequencing data showed that 42% of the mutations detected in the DNA are expressed. Interestingly we observed the progressive loss of wildtype FAM46C alleles, which are often heterozygously mutated and expressed in patients but is exclusively homozogous in cell lines. Additional integrated analysis will improve out understanding of myelomagenesis and might lead to new and improved classification and prognostic models. Citation Format: Jonathan Keats, Venkata Yellapantula, Winnie Liang, Ahmet Kurdoglu, Angela Baker, Joan Levy, Todd Golub, David Craig, John Carpten. RNA sequencing based prognostic indexes show a high degree of correlation with historic array based gene expression measurements in multiple myeloma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 806. doi:10.1158/1538-7445.AM2013-806

Genome Wide Studies in Multiple Myeloma Identify XPO1/CRM-1 As a Critical Target Validated Using the Selective Inhibitor of Nuclear Export (SINE) KPT-276

AbstractAbstract 573Using high throughput RNA interference screening on 6,722 druggable genes we previously identified XPO1/CRM1 as one of the 50 most vulnerable targets in Multiple Myeloma (MM)1. XPO1 knockdown proved lethal in MM cell lines, but had no effect on human embryonic kidney (293) cells or lung cancer (A549) cells, showing that XPO1 is a specific myeloma vulnerability, and that myeloma cell survival is dependent upon XPO1 expression. XPO1 encodes the protein exportin 1, a nuclear transport protein that exports tumor suppressor proteins from the nucleus, where they are active, to the cytoplasm, where they become inactive. We next analyzed XPO1 in MM via gene expression profiling (GEP). XPO1 expression is up-regulated as the disease progresses: patients with active MM have a higher level of XPO1 compared to normal plasma cells (p<0.04) and to patients with monoclonal gammopathy of undetermined significance or smoldering MM (p<0.0001). The highest levels were in human MM cell lines. TC classification revealed highest levels in t(11;14) and lowest levels in t(4;14) disease.Selective inhibitors of nuclear export (SINE) compounds have recently been developed that irreversibly inhibit XPO1/CRM1 and its nuclear export function. One such inhibitor, KPT-276, decreased the viability of all 12 MM cell lines tested in vitro, as shown by MTT assay. After 72 hours of drug treatment, a median IC50 value of approximately 175 nM (range 30–1000 nM) was observed. No synergy with other commonly used anti-MM therapeutics was observed in vitro. In contrast, the drug had little effect in 8 solid tumor cell lines with the exception of the B cell lymphoma line Ramos. KPT-276 was also consistently active in inducing apoptosis against MM primary patient samples. Using an IC80 dose of KPT-276, drug-treated samples had a reduced population of cells in S phase (8%) compared to cells treated with DMSO (21%). Using the vkappa*myc transgenic MM model, KPT-276 reduced monoclonal spikes (by a mean of 56%) in all mice treated orally with 150 mg/kg dose three times per week for 4 weeks. Furthermore, KPT-276 significantly reduced tumor growth in a xenograft MM1.S mouse model. GEP was performed in the presence or absence of drug in two different MM cell lines. Two genes of probable relevance, cell division cycle 25 homolog A (CDC25A) and Bromodomain-containing protein 4 (BRD4), were dysregulated by SINE treatment. Both are involved in cell cycle control and have been linked to MYC. RT-PCR and western blotting confirm that MYC, CDC25A and BRD4 are down-regulated, as soon as six hours, after treatment with KPT-276. KPT-276 has shown marked anticancer activities against B cell malignancies in vitro and is active and tolerated in Phase I canine studies. KPT-330, a close analog of KPT-276, is currently in Phase 1 studies in human with advanced hematological and solid tumors. Disclosures:Schmidt:Karyopharm: Research Funding. McCauley:Karyopharm Therapeutics Inc: Employment. Shacham:Karyopharm Therapeutics: Employment. Kauffman:Karyopharm Therapeutics Inc: Employment. Stewart:Millenium: Consultancy, Honoraria, Research Funding; Onyx: Consultancy; Celgene: Consultancy.

Generation of An Automated Tool for the Identification of Genetics Markers and Signatures in Multiple Myeloma Risk-Stratification

Abstract 2881Multiple myeloma (MM) is characterized by a remarkable heterogeneity in outcome following standard and high-dose therapies. Significant efforts have been made to identify genetic changes and signatures that can predict clinical outcome and include them in the routine clinical care. Gene expression profiling (GEP) studies have achieved a central role in the study of multiple myeloma (MM), as they become a critical component in the risk-based stratification of the disease. To molecularly stratify disease-risk groups, we performed GEP on purified plasma cells (obtained from the immunobead selection of CD138+ cells) from 489 MM samples in different stages of the disease using the Affymetrix U133Plus2.0 array. A total of 162 probes were analyzed using an in house automated script to generate a GEP report with the most used risk stratification indices and signatures, including the UAMS 70-gene, UAMS class, TC classification, proliferation and centrosome signature, and NFKB activation indices. In a subset of 57 samples, IgH translocations were analyzed using FISH and results were correlated with GEP data. A macrophage index was calculated and used as a surrogate measurement of non-plasma cell contamination. A total of 49 samples (10%) were excluded from subsequent analysis as the macrophage index indicated a significant contamination with no plasma cells, hence potentially compromising the results. The percent of high-risk disease patients identified from different signatures ranged from 26.4% by using high proliferation index to 28.8% with high centrosome signature and 31.3% with high 70-gene index. This percent of high-risk cases based on the 70-gene index is similar to what was found in Total therapy 2 (TT2) and TT3 cohorts. A third of patients (33.2%) were classified as D1 in the TC class, followed by 11q13 (19.3%), D2 (16.4%), 4p16 (13.8%), MAF (6.1%), None (4.7%), D1+D2 (4.5%) and 6p21 (1.8%). The NF-kB pathway was likely activated in 45.5% to 59.5% of cases, depending on the index used for its calculation. High proliferation index and high centrosome signature significantly correlates with 70-gene high-risk group (p<0.0001). Conversely, the activation of NF-kB pathway was not significantly different between high- and low- risk subgroups. TC subgroups D1 (p<0.0001) and 11q13 (p=0.01) were significantly more common in the 70-gene low-risk group. Similarly, TC subgroups 4p16 (p=0.0004), Maf (p=0.02) and D2 (p=0.05) were enriched in the high-risk group. Translocations t(4;14)(p16;q32), t(11;14)(q13;q32) and t(14;16)(q32;q23) were precisely predicted by the TC classification (100% correspondence). Cases with IgH translocations with unknown partner were classified in subgroups D1 (33%), D2 (25%), 6p21 (25%) and Maf (16%). Here we summarized the associations between the most significant gene expression indices and signatures relevant to MM risk-stratification. The multiple variables simultaneously analyzed in an automated way, provide a powerful and fast tool for risk-stratification, helping in the therapeutic decision-making. Disclosures:Stewart:Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Onyx Pharmaceuticals: Consultancy, Research Funding. Fonseca:Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.

ETV1 Is a Survival Gene That Is Expressed in a Subset of Multiple Myeloma

Abstract 2884While several genes have been shown to participate in primary Ig translocations in about 50% of cases of multiple myeloma, it is still remains unclear how activation of these genes ultimately leads to a transformed phenotype. In nearly 15% of myeloma cases, t(4;14) results in the over-expression of MMSET and FGFR3. However the FGFR3 translocation is lost in about 25% of these patients. To identify genes associated with these changes we initially compared the gene expression profiles (GEP) of t(4;14) patients that expressed FGFR3 with those that did not. Using both supervised and unsupervised hierarchal clustering we were unable to identify significant differences associated with FGFR3 expression. Therefore we pooled these samples and compared them to GEP from normal plasma cells and MGUS patients to identify genes that were up-regulated in t(4;14) myelomas. As validation of this approach the two highest up-regulated genes were FGFR3 and MMSET (WHSC). Interestingly an additional gene that was significantly up-regulated (33 fold) was the ETS family transcription factor Ets variant gene 1 (ETV1; also called ER81). Further analysis of GEP data of MM patient samples revealed that ETV1 is primarily expressed in the genomically defined CD1, CD2 and MS subgroups in newly diagnosed patients (Arkansas dataset GSE4204). These data were confirmed in relapsed/refractory patients where expression was greatest in the 11q13 and 4p16 subsets (Tc classification, Mulligan dataset GSE9782). ETV1 is a member of the ETS family transcription factors and has been shown to be involved in recurrent translocations in prostate cancer and is also up-regulated in GIST. However no data exists on the role of ETV1 in MM. We investigated the expression of ETV1 in human myeloma cell lines (HMCL) and determined the effect of silencing on cell viability, proliferation, and drug responses.To determine whether ETV1 is expressed in MM cell lines, 10 HMCL were subjected to real-time PCR and western blot analysis. All ten cell lines expressed ETV1 with MM.1s expressing the lowest levels. Consistent with the GEP data from the patient samples, the t(4;14) and t(11;13) lines expressed 5.5–13-fold higher levels of ETV1 than MM.1s. Surprisingly the highest expresser was not from one of these translocation groups, as RPMI8226 displayed a 74-fold increase compared with MM.1s. Determination of the potential reason for this level of over-expression is currently underway. Protein levels of ETV1 also correlated with mRNA level for all MM cell lines. To functionally characterize the role of ETV1 in myeloma, we utilized shRNA-mediated knockdown in the KMS11 line. ETV1 knockdown had no effect on cell proliferation, but did result in increased apoptosis compared to the vector control KMS11 cells, suggesting a biological role for ETV1 in myeloma cell survival. Consistent with this possibility, we observed increased sensitivity to the Bcl-2 inhibitor ABT-737 and the HDAC inhibitors SAHA and Romidepsin in ETV1-shRNA bearing KMS11 when compared to vector control. These results provide the first line of evidence that ETV1 plays a significant role in a subset of MM and suggest that therapies targeting this gene could have an impact in a subset of MM patients. Disclosures:No relevant conflicts of interest to declare.

Comprehensive High-Resolution Studies Differentiate and Characterize Hypodiploid Multiple Myeloma

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.

Generation of an automated tool for querying myeloma transcriptomics for multiple gene expression signatures used in risk-stratification.

8024 Background: Multiple myeloma (MM) is characterized by a remarkable heterogeneity in outcome following standard and high-dose therapies. In MM, gene expression profiling (GEP) has a key role in establishing clinically relevant disease subgroups. Methods: To molecularly stratify disease-risk groups, we performed GEP on purified plasma cells from 469 MM cases in different stages of the disease using the Affymetrix U133Plus2.0 array. A total of 162 probes were analyzed using an in house automated script to generate a GEP report with the most used risk stratification indices and signatures, including the UAMS 70-gene, TC classification, proliferation and centrosome signature, and NFKB activation indices. In a subset of samples, IgH translocations were analyzed using FISH and results were correlated with GEP data. Results: All signatures identified around a third of patients with high-risk disease, ranging from 26% by using high proliferation index to 28% with high centrosome signature and 31% with high 70-gene index. High proliferation index and high centrosome signature significantly correlates with 70-gene high-risk group (p<0.0001). Conversely, the activation of NF-kB pathway was not significantly different between high- and low- risk subgroups. TC subgroups D1 (p<0.0001) and 11q13 (p=0.01) were significantly more common in the 70-gene low-risk group. Similarly, TC subgroups 4p16 (p=0.0004), Maf (p=0.02), D2 (p=0.05), and None (p=0.08) were enriched in the high-risk group. Translocations t(4;14)(p16;q32), t(11;14)(q13;q32) and t(14;16)(q32;q23) were precisely predicted by the TC classification (100% correspondence). Cases with IgH translocations without known partner were classified in subgroups D1 (33%), D2 (25%), 6p21 (25%) and Maf (16%). Conclusions: Here we summarized the associations between the most significant gene expression indices and signatures relevant to MM risk-stratification. The multiple variables simultaneously analyzed in an automated way, provide a powerful and fast tool for risk-stratification, helping in the therapeutic decision-making.

Multiple Myeloma Patients Who Are Chemosensitive and Present with Plasma Cells Lacking Phosphorylated Retinoblastoma and/or Cyclin A Expression Benefit From High Dose Therapy and Autologous Stem Cell Transplantation More.

AbstractAbstract 1345 Background and aim:Multiple Myeloma (MM) plasma cells are known to posses low replicative potential and plasma cell labelling index is generally accepted as a strong prognostic factor. TC classification of MM defines categories of myeloma cases expressing different cyclins (Fonseca et all, Leukemia 2009). Although cyclin dependent kinase inhibitors (CDKI) are important for cell cycle, they are not included in this molecular classification. Patients and Methods:With an aim to define high and low proliferative myeloma cases antibodies detecting cyclins (Cyc) A, D1, D2, D3, phosphorylated retinoblastoma (Rb), p16, p21, p27, Ki67 were applied to tissue sections obtained from either marrow plasmacytoma of 106 consequtive myeloma patients (median: 59, 32–79 years) diagnosed between 1998–2007. 100 patients <65 years (n:51) were evaluable for treatment outcome and received an induction of VAD (4-6 cycles), followed by a novel agent containing regimen (2-4 cycles) or autologous stem cell transplant (ASCT) depending on the response to induction. Postransplant consolidation or subsequent transplants were performed when < VGPR was obtained. Elderly patients (n:49) received induction for at least a year. Novel agents (Thalidomide: 66.7%, Bortezomib: 27.5%) were given in 94.2% of patients as ≥2 line treatment. Mann Whitney U, Kaplan-Meier or log rank analysis were performed using the SPSS version 15.0. Results:Loss of CDKI's were detected in 55.4–88.5 % of the cases. Cyclin D1D2D3 or Cyclin A expression was detected in 29.2, 21.7, 5.7 or 19.8 % of the cases. Rb was detected in 25%. Among the CDK's or CDKI's only Cyc D2 was correlated with Ki67 percentage (p=0.027). Loss of all CDKI's was observed in 31.1 %. The majority of p16 (-) cases were Cyc A (-) too (p=0.001). Loss of p16 and p 27 was observed more frequently than loss of p 21 (88 and 85% versus 54%). Patients were either both CycA and p21 (-) (50%) or both CycA and p21 (+) (31 %) (p=0.025). However there was no patient expressing Rb and p16. On the contrary, the majority patients were both Rb and p16 (-) (60%) (p=0.037). Groups of patients with high proliferative protential group 1 (Cyc D+ p16-), group 2 (CycA+ p21-), group 3 (CycA+ Rb+) and low proliferative protential group 4 (Rb- p16+), group 5 (CycA- Rb-) were analyzed. These were observed 42.5 %, 5.5%, 11.7%, 15.4% and 57.1%. No correlation could be found between the CDKI/Cyc defined risk groups and ISS, B2MG, LDH, CRP, OS. However among p16 (-) cases Rb (-) ones (n:39) had longer OS than Rb (+) patients (n:18) (49 vs 39 months). All p16 (+) patients were Rb (-) too and had a shorter OS (42 vs 84 months, p=0.006). Response to initial treatment was ≥ VGPR 16.8 %, ≥ PR 52%, refractory 21.4 %. High proliferative group 1 or patients with high LDH values had higher initial response rates (82.1 vs 53.8 %, p: 0.016 and 67.6 % vs 36 % p= 0.018). The deepest response was observed with initial treatment among 47.3 % and was improved following ASCT (18.3%). ASCT also improved OS among all (58 vs 34 months, p=0.0) but more strongly among Rb and/or CycA (-) patients (Table). The response to initial treatment did not influence OS. However if response is not upgraded following subsequent treatments OS deteriorated (5 year OS: 34 vs 44 months p=0.022) (Figure). These suboptimal responding patients could not be predicted by ISS, age, B2MG, high/low proliferating group definition. Although LDH was high in 72.7 vs 54.5 % of poor-responders this comparison was not significant. Conclusion:While depth of response to therapy did not, improvement of response with subsequent therapy ie novel agents or ASCT prolongged OS (34 vs 44 months, p=0.022) and was associated with lower LDH values. Compared to gene expression profilling, tissue array using monoclonal antibodies is a method which can be more widely applicable and in our study was able to define new prognostic parameters. Overall ASCT extended OS. This effect was stronger among patients lacking Rb and/or Cyc A, This finding echoes the need for better treatment modalities for patients with poorer prognosis ie Rb and/or Cyc A positivity. Multivariate analysis results will be presented during the congress.TablePresence of cell cycle related markers and overall survival (months)nmonthsASCT (-)Cyc A (+)831,6Cyc A (-)4171Rb (+)1658,2Rb (-)3255,6Cyc A (+) Rb (+)329Cyc A (-) Rb (-)2330,1ASCT (+)Cyc A (+)1261,9Cyc A (-)3991,3Rb (+)1154,4Rb (-)4091,5Cyc A (+) Rb (+)646Cyc A (-) Rb (-)3456,9 [Display omitted] Disclosures:No relevant conflicts of interest to declare.

Tropical Cyclone Inner-Core Kinetic Energy Evolution

Abstract Tropical cyclone (TC) destructive potential is highly dependent on the distribution of the surface wind field. To gain a better understanding of wind structure evolution, TC 0–200-km wind fields from aircraft reconnaissance flight-level data are used to calculate the low-level area-integrated kinetic energy (KE). The integrated KE depends on both the maximum winds and wind structure. To isolate the structure evolution, the average relationship between KE and intensity is first determined. Then the deviations of the KE from the mean intensity relationship are calculated. These KE deviations reveal cases of significant structural change and, for convenience, are referred to as measurements of storm size [storms with greater (less) KE for their given intensity are considered large (small)]. It is established that TCs generally either intensify and do not grow or they weaken/maintain intensity and grow. Statistical testing is used to identify conditions that are significantly different for growing versus nongrowing storms in each intensification regime. Results suggest two primary types of growth processes: (i) secondary eyewall formation and eyewall replacement cycles, an internally dominated process, and (ii) external forcing from the synoptic environment. One of the most significant environmental forcings is the vertical shear. Under light shear, TCs intensify but do not grow; under moderate shear, they intensify less but grow more; under very high shear, they do not intensify or grow. As a supplement to this study, a new TC classification system based on KE and intensity is presented as a complement to the Saffir–Simpson hurricane scale.

Genomic Complexity and Recurrent Aberration in Multiple Myeloma: Analysis of a Compendium of aCGH and Gene Expression Profiles.

Multiple Myeloma (MM) is the second most prevalent haematological disorder in the US with great recent progress propelled by various technological and therapeutic advances. Nevertheless, the complexity of MM genomes remains inadequately characterized. We have combined high-resolution (44K Agilent) array comparative genomic hybridization (aCGH) and gene expression data with clinical outcomes in an initial attempt at indexing genomic complexity in MM and its clinical implication in 100 MM patients. We enumerated the Number of Aberrations per Tumor (NAPT) as defined by the ADM-1 algorithm available within CGH Analytics. There appear to be no difference in degree of genetic complexity between hyperdiploid and non-hyperdiploid myeloma. Generally, t(11;14) have low NAPT whereas D2 tumors have high NAPT. Amongst the high-risk genetic subtypes, tumors with maf translocations have relatively low NAPT whereas t(4;14) have high NAPT. Whilst a Multivariate Cox Proportional Hazards regression of survival outcome showed little statistical support for popular indicators such as ploidy and TC classification, results for NAPT were considerably more encouraging (p~ 0.07, median survival of 40 months in low complexity vs 18 months in high). Log-rank tests further supported prognosis based on NAPT independent of clinical status and ploidy. To characterize complexity, we clustered aCGH profiles by clinical status, ploidy and TC classification. Clear patterns of aggregation and recurrent aberration were observed suggesting that they were possibly instrument to aberrant gene expression. Indeed, expression of a large proportion of genes approximately 2Mb of the recurrent breakpoints varied significantly between patients carrying aberrations vis-a-viz others that did not. Comparison against known gene sets showed an enrichment for cell-cycle and apoptosis genes. We are presently characterizing several such breakpoint-associated genes for their functional significance.

Bone Involvement in Multiple Myeloma Patients Carrying the T (4;14) Chromosomal Translocation.

Chromosomal translocations involving the immunoglobulin heavy chain switch region (IgH) are quite common in multiple myeloma (MM), and some of them can reliably predict disease outcome. In particular, t(4;14) chromosomal abnormality is one of the most adverse prognostic factors for response duration and survival after high dose therapy and autologous stem cell transplantation. Despite the dismal prognosis, however, in this subset of patients, bone involvement, as evaluated by spine MRI, is relatively infrequent, at variance to what has been observed in other MM subtypes according to TC classification. In the present study we aimed at further testing this hypothesis by analyzing the extent of whole bone involvement in patients showing t(4;14) chromosomal translocation as compared to negative patients. For this purpose, 50 newly diagnosed MM patients (32M, 18F, median age = 54 yrs) underwent evaluation of total skeletal X-ray, whole spine MRI and, at the same time, quantification of markers of bone resorption (urinary NTX, PYR and DPYR and serum crosslaps) and bone formation (bone alkaline phosphatase-BAP and osteocalcin) was performed. Using a real-time PCR assay to detect the presence of IgH/MMSET fusion gene as a surrogate marker for t(4;14), we found 15 patients carrying this chromosomal abnormality, 7 of whom (46%) were also positive for the deletion of chromosome 13, this abnormality was detected in 11/35 (31%) patients who proved negative for IgH/MMSET hybrid transcript. The two groups of patients did not differ significantly in terms of median age, distribution of M protein isotype and light chain, beta-2 microglobulin, bone marrow plasma cell infiltration and disease stage. Spinal MRI was negative in 3/15 (20%) t(4;14) positive patients as compared to 12% t(4;14) negative patients; skeletal involvement, however, was more pronounced in t(4;14) positive patients (median skeletal score = 6.24, as compared to 3.58 in t(4;14) negative cases, p= 0.00). These data were confirmed by the evaluation of bone resorption markers; specifically, serum crosslaps were significantly increased in patients with t(4;14) abnormality compared to negative individuals (7984 pmol/L±1682SE vs 5123pmol/L±783SE p=0.04). Conversely, no difference in bone formation markers was found in the two groups of patients. Our results indicate that, despite a spinal involvement at MRI that is comparable to what is observed in negative patients, individuals who are t(4;14) positive show a more pronounced bone resorption pattern, this in contrast to what has been reported so far, but in line with the aggressive features of the disease in these patients.

Fine Mapping and Expression Analysis of Chromosome 1 with the Aim of Defining Critically Deregulated Genes Important in the Pathogenesis of Myeloma.

Deletions on chromosome 1p and gains on chromosome 1q are common alterations in Myeloma (MM). We analyzed 78 cases using Affymetrix 500K SNP and U133 Plus 2 expression arrays to characterize abnormalities of this chromosome. Copy number, loss of heterozygosity (LOH) analysis and supervised hierarchical clustering (HC) were performed using dChipSNP. LOH and deletion of 1p were found in 22/78 cases (28%). Of these 6 had deletion of the whole of 1p, 9 cases had an interstitial deletion (1p12–p21.3), median size 47.2 Mb (0.2–50.5), and 7 cases had more than one interstitial deletion, median size 5.3 Mb (0.5–55.6). Supervised HC of expression data identified 324 differentially expressed genes comparing deletions of 1p with normal cases. Of these 12 genes were located at 1p12–p21.3, all of which were underexpressed in the samples with deletion. We found one small homozygous microdeletion located at 1p32.3 containing only one transcript CDKN2C. Homozygous deletions by definition contain genes which are completely inactivated and as such are likely to be relevant to the MM pathogenesis. We extended this analysis looking for other alterations at this region and homozygous deletions, mean 0.41 Mb (0.03–1.74), were seen in 4 samples at 1p32.3 (5%). These cases together with another 6 (13%) with deletion at 1p32.3 define a minimally altered region within which CDKN2C is located and in all cases is underexpressed. This data suggests that inactivation of the INK4a/ARF pathway may be important. We looked at other genes in this pathway but did not find homozygous deletions in any of them, however, we did identify another 5 cases with hemizygous deletion. The TC classification utilises data on cyclin D expression and as CDKN2C directly regulates these complexes we looked at how these changes were distributed relative to expression of cyclin Ds. We did not find a consistent pattern as alterations were found in both the elevated D1 and D2 groups. Further analysis of the expression patterns in this pathway and how they relate to genomic changes detected with the SNP array is ongoing. Gains of 1q were identified in 25/78 cases (32%), with gain of the whole of 1q being responsible for the majority. Supervised HC between samples with and without 1q gains identified 659 differentially expressed genes. Of these, 83 genes were located in 1q21–q43, all of which were overexpressed in 1q+. We identified a single case with a small defined region of gain (up to 4 copies) covering 17.5 Mb at 1q21.1–q23.3. Looking within this region following HC we found 42 genes overexpressed (1.3–32.4 fold), including MCL1 and NTRK1, compared to other 1q+ cases. We analyzed our dataset for previously described genes altered in 1q. While we found upregulation of CKS1B, BCL9 and PDZK1 we could not distinguish these from other genes located within this amplicon. Gain of 1q has been associated with poor outcome and we sought other regions co-segregating with 1q+; del13 was the most frequently associated change and all cases with both alterations had an IGH translocation, which may contribute to the poor prognosis of this subgroup. We confirm the importance of changes on chromosome 1 in the MM pathogenesis and provide data supporting the critical deregulation of the G1S cell cycle transition as being important and frequently affected by deletions of 1p affecting CDKN2C.