Henry, Travis

Abstract Rationale Mucus plugs occur in chronic obstructive pulmonary disease (COPD), but little is known about their size and airway location or whether these mucus plug features change as emphysema worsens. Methods Computed tomographic lung scans from 31 participants with COPD from the SubPopulations and InteRmediate Outcomes In COPD Study (SPIROMICS) cohort were analyzed to quantify mucus plug size and airway location and extent of emphysema. Radiologist annotations of mucus plugs were incorporated in an image processing pipeline to generate size and location information. Emphysema was quantified as the percentage of voxels less than -950 Hounsfield units. Measurements and Main Results The length distribution of 563 annotated mucus plugs varied from 2 to 50 mm. The distribution was multimodal, and an 11-mm length defined short (“stubby,” ≤11 mm) and long (“stringy,” >11 mm) plug phenotypes, with stubby plugs being most common (64%). Mucus plugs localized predominantly to airway generations 6 to 9, and their prevalence was highest in lower lobes. On a lobar basis, the mucus plug number decreased as emphysema percentage increased, whereas average mucus plug volume tended to increase. Conclusion COPD is characterized by mucus plugs of varying size and shape, and emphysematous lung regions may be differentially susceptible to formation of these plugs. The location of mucus plugs in segmental and subsegmental airways in COPD makes them amenable to treatment with inhaled medications or bronchoscopy.

Abstract Chromosome 13 deletion (Δ13) is one the most common genetic abnormalities observed in multiple myeloma (MM) and confers a poor prognosis. Studies to identify critical tumor suppressor genes in MM till date have lack adequate resolution, and the molecular phenotype associated with Δ13 has not been established. In this study we seek to establish a molecular profile for Δ13 and to finely map the minimal common region of deletion (CDR) on chromosome 13 by using gene expression profiling (GEP) and array comparative genomic hybridization (aCGH). GEP was performed on RNA from purified plasma cells of 72 newly diagnosed MM and 50 human myeloma cell lines (HMCLs) using the Affymetrix U133A chip and U133plus chip (Affymetrix, Santa Clara, CA) respectively. Patients were assigned TC classes which correlates with underlying genetic subtypes. In addition, aCGH was performed on 79 MM samples (36 with GEP data) and 50 myeloma cell lines (HMCLs; 48 with GEP data) using a platform utilizing 60-mer oligonucleotides (Human Genome CGH 44B Oligo Microarrays, Agilent Technologies), which have a resolution of about 70Kb. Raw data was extracted using the Feature Extraction 8.1 and visualized using CGH Analytics 3.2 (Agilent Technologies). To define a signature that reflects the biological consequences of Δ13 and not its close association with some genetic subtypes of MM (example, t(4;14) and t(14;16)), we selected a training cohort consisting of cases belonging to D1, D2, 11q13 and none TC class such that cases with and without Δ13 are balanced for ploidy and TC classes (n=28). A 152-gene Δ13 signature was identified. Its specificity was confirmed by leave-one-out cross validation using the K-nearest neighbor (KNN) class prediction algorithm (predictive accuracy of 100% in the training cohort). It was subsequently validated in a validation cohort that includes t(4;14) and t(14;16) that were not use to derive this gene signature, maintaining a high predictive accuracy (88%). This gene-set may therefore reflect core transcriptional consequences of Δ13 and is enriched for genes involved in the cell cycle and apoptosis. Using aCGH, 20 patients have Δ13 (mostly whole chromosome) all involving the 13q14–q21 region. Of the 32 HMCLs with Δ13 (whole chromosome or interstitial), 22 have mono-allelic loss of RB1 whereas 4 had bi-allelic loss. Besides RB1, bi-allelic loss of several other genes in the 13q14–q21 region (CYSLTR2, CDADC1, ITM2B, PCDH9) was also observed. As the CDR is large and contains a number of genes, we utilized a set of criteria (bi-allelic loss in HMCLs, within 13q14–q21 CDR, gene expression significantly correlated with copy number, and loss could explain molecular phenotype) to narrow down the potential candidates. Only RB1 and ITM2B fulfilled these criteria. Furthermore, RB protein levels correlated well with mRNA levels and DNA copy suggesting that even in absence of mutations resulting in bi-allelic loss, mono-allelic loss of RB1 could be tumorigenic through a haploinsufficiency mechanism.

Abstract Deletion 13 multiple myeloma (MM) is detected in nearly 50 % of patients diagnosed with MM and confers a shorter survival. A region of minimal deletion was identified for chromosome 13 using Agilent 500K aCGH arrays that included microRNAs 15a and 16- 1. MicroRNAs (miRs) are small RNAs that negatively regulate gene expression through degradation of mRNA transcripts or translational inhibition. In order to determine the contribution of deletion of miRs 15a and 16-1 to MM progression, miR precursors were transfected into KMS-11 and JJN3 adherent myeloma cell lines and total RNA hybridized to Affymetrix U133 Plus 2.0 gene expression arrays for the purpose of identification of mRNA target transcripts. Thirty nanomolar miR 15a and 16-1 precursors and a nonsilencing siRNA control were transfected into adherent KMS-11 and JJN3 myeloma cell lines. Cultures were harvested 16 hours after transfection to minimize the downregulation of transcripts that are not direct targets of miRs 15a and 16-1. Total RNA was extracted using the miRNeasy kit to allow retention of the miR fraction for RT-PCR confirmation of miR over-expression following transfection. Following transfection of miR precursors, expression of miRs 15a and 16-1 were increased 64 and 128-fold, respectively, compared to non-silencing control. Total RNA was hybridized to Affymetrix gene expression arrays using protocols supplied by the manufacturer. Transcripts down-regulated following miR transfection were compared to mathematical models for prediction of miR targets. Additionally, the 3′ UTRs of down-regulated transcripts were inspected for complementarity to miR 15a and 16-1 seed sequences. RT-PCR validation of identified targets was performed. Cross reference of down-regulated transcripts with the TargetSCAN and PictarVERT miR prediction algorithms resulted in a list of 9 genes that represented potential miR-15a/16-1 targets in MM. This list included: FGF2, BCL2, CCNE1, V-MYB, WEE1, E2F7, CDK6, CDC25A and CDC27. Following target identification, reporter constructs were used to confirm direct regulation of transcripts by miRs 15a and 16-1. Functional investigation of miR targets was performed using siRNA reduction of identified targets followed by MTT assay and cell cycle analysis.

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 Abstract 4984 Background: Waldenström macroglobulinemia (WM) is a B-cell lymphoproliferative disorder characterized by lymphoplasmacytic bone marrow infiltration, immunoglobulin M (IgM) monoclonal gammopathy and increased production of interleukin-6 (IL-6). Anemia occurs in a significant number of these patients and it is postulated that this occurs independently of tumor burden. Hepcidin is a hepatic peptide hormone that plays a central role in iron homeostasis and has been implicated in anemia of inflammation. Hepcidin binds to the cell-membrane iron exporter-ferroportin, causing its internalization and degradation, leading to iron deficiency anemia. The role of hepcidin in WM patients with anemia is not clearly understood. In this study we investigated the association between anemia, inflammation and hepcidin in WM patients with an eventual goal of providing improved treatment strategies for anemia in this complex set of patients. Patients and Methods: Fifty-nine WM patients [Female N=25, Median Age 70 (49-89), Hemoglobin (Hg) 11.1gm/dl, IgM 3420mg/dl, Beta-2 Microglobulin (β2M) 3.105mcg/ml and Bone Marrow Involvement 30%] and fifteen healthy patients [Female N=6, Median Age 36 (18-51)] were analyzed. The patients were divided into anemics (Hg <12, N=38) and non-anemics (Hg >12, N=21). The serum level of IL-6 was measured using Human IL-6 ELISA Kit II (BD Biosciences) and hepcidin using Hepcidin ELISA (DRG Instruments GmbH, Germany) with protocols supplied by the manufacturers. Tumor cells from patients with WM were sorted by either CD19 or CD138 expression, RNA extracted and hybridized to Affymetrix U133A 2.0 expression arrays. Gene expression analysis was performed using Agilent Genespring GX 11.0 software. Gene expression data was summarized using GCRMA and median normalized per gene. Statistical analyses were performed by utilizing student t tests and One-way ANOVA using PRISM software. Results: WM patients with anemia had significantly higher levels of IgM (4121 vs. 2845mg/dl, p=0.021), beta-2 microglobulin (3.77 vs. 2.72mcg/ml, p=0.009) and bone marrow involvement (41.45 vs. 27.14%) as opposed to non-anemics. IL-6 levels were significantly higher in anemics when compared to non-anemics and controls (6.486 vs. 1.952 vs. 1.716 pg/ml, p=0.0433). Further, in WM patients with anemia serum hepcidin levels were significantly elevated when compared to non-anemics (13.03 vs. 6.08ng/ml, p=0.0123). An additional comparison was made with severe anemics (Hg <10g/dl) to non-anemics and higher mean hepcidin levels were noted in the former (16.21 vs. 6.08 gm/dl, p=0.0057). Gene expression analysis was then performed on 12 patients identified as severe anemics (Hg < 10g/dl) or non-anemics (Hg > 12g/dl). Unsupervised clustering identified 2 major groups that separated anemics versus non-anemics. Further, unsupervised clustering segregated anemic patients with the largest IgM values, suggesting that the expression and subsequent clustering of genes was driven by the level of tumor burden in this patient cohort. Also, up-regulation of genes involved in the cell cycle and proliferation such as cyclin A2, cyclin B1, PCNA and BUB1B were identified in anemic WM patients. Conclusion: A statistically significant increase in tumor burden (increased IgM, β2M and bone marrow involvement) was observed in WM patients with anemia as compared to patients without. A statistically significant increase in serum IL-6 was noted in WM patients with anemia. Interestingly, the WM patients with anemia were also noted to have higher serum hepcidin levels. Taken together, our data suggests that increased tumor burden may promote increased IL-6 and subsequently results in increased hepcidin, which correlates with anemia in WM patients. Additional studies are required to determine the specific role of hepcidin in anemia of WM. Anemia causes significant morbidity in WM patients; therefore, understanding the mechanism of anemia is important in providing superior patient care. Disclosures: Fonseca: Amgen: Consultancy; Bristol-Myers Squibb: Consultancy; Celgene: Consultancy, Research Funding; Genzyme: Consultancy; Onyx: Research Funding; Otsuka: Consultancy; Medtronic: Consultancy.