Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer.

BACKGROUND: High mitotic activity is associated with the genesis and progression of many cancers. Small molecule inhibitors of mitotic apparatus proteins are now being developed and evaluated clinically as anticancer agents. With clinical trials of several of these experimental compounds underway, it is important to understand the molecular mechanisms that determine high mitotic activity, identify tumor subtypes that carry molecular aberrations that confer high mitotic activity, and to develop molecular markers that distinguish which tumors will be most responsive to mitotic apparatus inhibitors. METHODS: We identified a coordinately regulated mitotic apparatus network by analyzing gene expression profiles for 53 malignant and non-malignant human breast cancer cell lines and two separate primary breast tumor datasets. We defined the mitotic network activity index (MNAI) as the sum of the transcriptional levels of the 54 coordinately regulated mitotic apparatus genes. The effect of those genes on cell growth was evaluated by small interfering RNA (siRNA). RESULTS: High MNAI was enriched in basal-like breast tumors and was associated with reduced survival duration and preferential sensitivity to inhibitors of the mitotic apparatus proteins, polo-like kinase, centromere associated protein E and aurora kinase designated GSK462364, GSK923295 and GSK1070916, respectively. Co-amplification of regions of chromosomes 8q24, 10p15-p12, 12p13, and 17q24-q25 was associated with the transcriptional upregulation of this network of 54 mitotic apparatus genes, and we identify transcription factors that localize to these regions and putatively regulate mitotic activity. Knockdown of the mitotic network by siRNA identified 22 genes that might be considered as additional therapeutic targets for this clinically relevant patient subgroup. CONCLUSIONS: We define a molecular signature which may guide therapeutic approaches for tumors with high mitotic network activity

Polyglutamine Expansion Mutation Yields a Pathological Epitope Linked to Nucleation of Protein Aggregate: Determinant of Huntington's Disease Onset

Polyglutamine (polyQ) expansion mutation causes conformational, neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. These diseases are characterized by the aggregation of misfolded proteins, such as amyloid fibrils, which are toxic to cells. Amyloid fibrils are formed by a nucleated growth polymerization reaction. Unexpectedly, the critical nucleus of polyQ aggregation was found to be a monomer, suggesting that the rate-limiting nucleation process of polyQ aggregation involves the folding of mutated protein monomers. The monoclonal antibody 1C2 selectively recognizes expanded pathogenic and aggregate-prone glutamine repeats in polyQ diseases, including Huntington's disease (HD), as well as binding to polyleucine. We have therefore assayed the in vitro and in vivo aggregation kinetics of these monomeric proteins. We found that the repeat-length-dependent differences in aggregation lag times of variable lengths of polyQ and polyleucine tracts were consistently related to the integration of the length-dependent intensity of anti-1C2 signal on soluble monomers of these proteins. Surprisingly, the correlation between the aggregation lag times of polyQ tracts and the intensity of anti-1C2 signal on soluble monomers of huntingtin precisely reflected the repeat-length dependent age-of-onset of HD patients. These data suggest that the alterations in protein surface structure due to polyQ expansion mutation in soluble monomers of the mutated proteins act as an amyloid-precursor epitope. This, in turn, leads to nucleation, a key process in protein aggregation, thereby determining HD onset. These findings provide new insight into the gain-of-function mechanisms of polyQ diseases, in which polyQ expansion leads to nucleation rather than having toxic effects on the cells

The use of gamma-irradiation and ultraviolet-irradiation in the preparation of human melanoma cells for use in autologous whole-cell vaccines

<p>Abstract</p> <p>Background</p> <p>Human cancer vaccines incorporating autologous tumor cells carry a risk of implantation and subsequent metastasis of viable tumor cells into the patient who is being treated. Despite the fact that the melanoma cell preparations used in a recent vaccine trial (Mel37) were gamma-irradiated (200 Gy), approximately 25% of the preparations failed quality control release criteria which required that the irradiated cells incorporate ³H-thymidine at no more than 5% the level seen in the non-irradiated cells. We have, therefore, investigated ultraviolet (UV)-irradiation as a possible adjunct to, or replacement for gamma-irradiation.</p> <p>Methods</p> <p>Melanoma cells were gamma- and/or UV-irradiated. ³H-thymidine uptake was used to assess proliferation of the treated and untreated cells. Caspase-3 activity and DNA fragmentation were measured as indicators of apoptosis. Immunohistochemistry and Western blot analysis was used to assess antigen expression.</p> <p>Results</p> <p>UV-irradiation, either alone or in combination with gamma-irradiation, proved to be extremely effective in controlling the proliferation of melanoma cells. In contrast to gamma-irradiation, UV-irradiation was also capable of inducing significant levels of apoptosis. UV-irradiation, but not gamma-irradiation, was associated with the loss of tyrosinase expression. Neither form of radiation affected the expression of gp100, MART-1/MelanA, or S100.</p> <p>Conclusion</p> <p>These results indicate that UV-irradiation may increase the safety of autologous melanoma vaccines, although it may do so at the expense of altering the antigenic profile of the irradiated tumor cells.</p

Retinoid Signaling in Pancreatic Cancer, Injury and Regeneration

Background: Activation of embryonic signaling pathways quiescent in the adult pancreas is a feature of pancreatic cancer (PC). These discoveries have led to the development of novel inhibitors of pathways such as Notch and Hedgehog signaling that are currently in early phase clinical trials in the treatment of several cancer types. Retinoid signaling is also essential for pancreatic development, and retinoid therapy is used successfully in other malignancies such as leukemia, but little is known concerning retinoid signaling in PC. Methodology/Principal Findings: We investigated the role of retinoid signaling in vitro and in vivo in normal pancreas, pancreatic injury, regeneration and cancer. Retinoid signaling is active in occasional cells in the adult pancreas but is markedly augmented throughout the parenchyma during injury and regeneration. Both chemically induced and genetically engineered mouse models of PC exhibit a lack of retinoid signaling activity compared to normal pancreas. As a consequence, we investigated Cellular Retinoid Binding Protein 1 (CRBP1), a key regulator of retinoid signaling known to play a role in breast cancer development, as a potential therapeutic target. Loss, or significant downregulation of CRBP1 was present in 70% of human PC, and was evident in the very earliest precursor lesions (PanIN-1A). However, in vitro gain and loss of function studies and CRBP1 knockout mice suggested that loss of CRBP1 expression alone was not sufficient to induce carcinogenesis or to alter PC sensitivity to retinoid based therapies. Conclusions/Significance: In conclusion, retinoid signalling appears to play a role in pancreatic regeneration and carcinogenesis, but unlike breast cancer, it is not mediated directly by CRBP1

The genome of the emerging barley pathogen Ramularia collo-cygni

Background Ramularia collo-cygni is a newly important, foliar fungal pathogen of barley that causes the disease Ramularia leaf spot. The fungus exhibits a prolonged endophytic growth stage before switching life habit to become an aggressive, necrotrophic pathogen that causes significant losses to green leaf area and hence grain yield and quality. Results The R. collo-cygni genome was sequenced using a combination of Illumina and Roche 454 technologies. The draft assembly of 30.3 Mb contained 11,617 predicted gene models. Our phylogenomic analysis confirmed the classification of this ascomycete fungus within the family Mycosphaerellaceae, order Capnodiales of the class Dothideomycetes. A predicted secretome comprising 1053 proteins included redox-related enzymes and carbohydrate-modifying enzymes and proteases. The relative paucity of plant cell wall degrading enzyme genes may be associated with the stealth pathogenesis characteristic of plant pathogens from the Mycosphaerellaceae. A large number of genes associated with secondary metabolite production, including homologs of toxin biosynthesis genes found in other Dothideomycete plant pathogens, were identified. Conclusions The genome sequence of R. collo-cygni provides a framework for understanding the genetic basis of pathogenesis in this important emerging pathogen. The reduced complement of carbohydrate-degrading enzyme genes is likely to reflect a strategy to avoid detection by host defences during its prolonged asymptomatic growth. Of particular interest will be the analysis of R. collo-cygni gene expression during interactions with the host barley, to understand what triggers this fungus to switch from being a benign endophyte to an aggressive necrotroph

Network-Guided Analysis of Genes with Altered Somatic Copy Number and Gene Expression Reveals Pathways Commonly Perturbed in Metastatic Melanoma

Cancer genomes frequently contain somatic copy number alterations (SCNA) that can significantly perturb the expression level of affected genes and thus disrupt pathways controlling normal growth. In melanoma, many studies have focussed on the copy number and gene expression levels of the BRAF, PTEN and MITF genes, but little has been done to identify new genes using these parameters at the genome-wide scale. Using karyotyping, SNP and CGH arrays, and RNA-seq, we have identified SCNA affecting gene expression (‘SCNA-genes’) in seven human metastatic melanoma cell lines. We showed that the combination of these techniques is useful to identify candidate genes potentially involved in tumorigenesis. Since few of these alterations were recurrent across our samples, we used a protein network-guided approach to determine whether any pathways were enriched in SCNA-genes in one or more samples. From this unbiased genome-wide analysis, we identified 28 significantly enriched pathway modules. Comparison with two large, independent melanoma SCNA datasets showed less than 10% overlap at the individual gene level, but network-guided analysis revealed 66% shared pathways, including all but three of the pathways identified in our data. Frequently altered pathways included WNT, cadherin signalling, angiogenesis and melanogenesis. Additionally, our results emphasize the potential of the EPHA3 and FRS2 gene products, involved in angiogenesis and migration, as possible therapeutic targets in melanoma. Our study demonstrates the utility of network-guided approaches, for both large and small datasets, to identify pathways recurrently perturbed in cancer

Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects.

Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies

Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects

Abstract Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies