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  • 1
    Keywords: APOPTOSIS ; PATHWAY ; GENES ; POOR-PROGNOSIS ; INCREASED EXPRESSION ; MICE LACKING ; high-throughput analysis ; ISLAND METHYLATOR PHENOTYPE ; D-DEPENDENT KINASES ; INK4 FAMILY
    Abstract: Uncontrolled cell cycle entry, resulting from deregulated CDK-RB1-E2F pathway activity, is a crucial determinant of neuroblastoma cell malignancy. Here we identify neuroblastoma-suppressive functions of the p19-INK4d CDK inhibitor and uncover mechanisms of its repression in high-risk neuroblastomas. Reduced p19-INK4d expression was associated with poor event-free and overall survival and neuroblastoma risk factors including amplified MYCN in a set of 478 primary neuroblastomas. High MYCN expression repressed p19-INK4d mRNA and protein levels in different neuroblastoma cell models with conditional MYCN expression. MassARRAY and 450K methylation analyses of 105 primary neuroblastomas uncovered a differentially methylated region within p19-INK4d. Hypermethylation of this region was associated with reduced p19-INK4d expression. In accordance, p19-INK4d expression was activated upon treatment with the demethylating agent, 2'-deoxy-5-azacytidine, in neuroblastoma cell lines. Ectopic p19-INK4d expression decreased viability, clonogenicity and the capacity for anchorage-independent growth of neuroblastoma cells, and shifted the cell cycle towards the G1/0 phase. p19-INK4d also induced neurite-like processes and markers of neuronal differentiation. Moreover, neuroblastoma cell differentiation, induced by all-trans retinoic acid or NGF-NTRK1-signaling, activated p19-/NK4dexpression. Our findings pinpoint p19-INK4d as a neuroblastoma suppressor and provide evidence for MYCN-mediated repression and for epigenetic silencing of p19-INK4d by DNA hypermethylation in high-risk neuroblastomas.
    Type of Publication: Journal article published
    PubMed ID: 25104850
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  • 2
    Keywords: BONE-MARROW ; BREAST ; MEMORY ; METASTASIS ; IMMUNOTHERAPY ; TNF-ALPHA ; INFILTRATION ; chemokines ; immune cells ; CELL RESPONSES
    Abstract: The composition of tumor-targeted T cell infiltrates is a major prognostic factor in colorectal cancer (CRC) outcome; however, the functional role of these populations in prolonging patient survival remains unclear. Here, we evaluated 190 patients with CRC for the presence of functionally active tumor-infiltrating lymphocytes (TILs), the tumor specificity of these TILs, and the correlation between patient TILs and long-term survival. Using intracytoplasmic cytokine staining in conjunction with HLA multimers loaded with tumor peptide and antigen-specific cytokine secretion assays, we determined that TNF-alpha expression delineates a population of tumor antigen-specific (TA-specific) cytotoxic T lymphocytes (CTLs) present within tumors from patients with CRC. Upregulation of TNF-alpha expression in TILs strongly correlated with an increase in the total amount of intratumoral TNF-alpha, which is indicative of tumor-specific CTL activity. Moreover, a retrospective multivariate analysis of 102 patients with CRC, which had multiple immune parameters evaluated, revealed that increased TNF-alpha concentration was an independent prognostic factor. Together, these results indicate that the prognostic impact of T cell infiltrates for CRC maybe largely based on subpopulations of active TA-specific T cells within the tumor, suggesting causal implication for these cells in patient survival. Additionally, these results support the use of intratumoral TNF-alpha, which is indicative of T cell function, as a prognostic parameter for CRC.
    Type of Publication: Journal article published
    PubMed ID: 25562322
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  • 3
    Keywords: GENE ; neuroblastoma ; ENHANCERS ; LANDSCAPE ; TERT REARRANGEMENTS
    Abstract: Neuroblastoma is a malignant paediatric tumour of the sympathetic nervous system. Roughly half of these tumours regress spontaneously or are cured by limited therapy. By contrast, high-risk neuroblastomas have an unfavourable clinical course despite intensive multimodal treatment, and their molecular basis has remained largely elusive. Here we have performed whole-genome sequencing of 56 neuroblastomas (high-risk, n = 39; low-risk, n = 17) and discovered recurrent genomic rearrangements affecting a chromosomal region at 5p15.33 proximal of the telomerase reverse transcriptase gene (TERT). These rearrangements occurred only in high-risk neuroblastomas (12/39, 31%) in a mutually exclusive fashion with MYCN amplifications and ATRX mutations, which are known genetic events in this tumour type. In an extended case series (n = 217), TERT rearrangements defined a subgroup of high-risk tumours with particularly poor outcome. Despite a large structural diversity of these rearrangements, they all induced massive transcriptional upregulation of TERT. In the remaining high-risk tumours, TERT expression was also elevated in MYCN-amplified tumours, whereas alternative lengthening of telomeres was present in neuroblastomas without TERT or MYCN alterations, suggesting that telomere lengthening represents a central mechanism defining this subtype. The 5p15.33 rearrangements juxtapose the TERT coding sequence to strong enhancer elements, resulting in massive chromatin remodelling and DNA methylation of the affected region. Supporting a functional role of TERT, neuroblastoma cell lines bearing rearrangements or amplified MYCN exhibited both upregulated TERT expression and enzymatic telomerase activity. In summary, our findings show that remodelling of the genomic context abrogates transcriptional silencing of TERT in high-risk neuroblastoma and places telomerase activation in the centre of transformation in a large fraction of these tumours.
    Type of Publication: Journal article published
    PubMed ID: 26466568
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  • 4
    Keywords: DISTRIBUTIONS ; leukemia ; PREDICTION ; REGRESSION ; PROPORTIONAL HAZARDS MODEL ; VARIABLE SELECTION ; LIKELIHOOD ; COORDINATE DESCENT ; SURVIVAL-TIME DATA ; REGULARIZATION PATHS
    Abstract: One important task in translational cancer research is the search for new prognostic biomarkers to improve survival prognosis for patients. The use of high-throughput technologies allows simultaneous measurement of genome-wide gene expression or other genomic data for all patients in a clinical trial. Penalized likelihood methods such as lasso regression can be applied to such high-dimensional data, where the number of (genomic) covariables is usually much larger than the sample size. There is a connection between the lasso and the Bayesian regression model with independent Laplace priors on the regression parameters, and understanding this connection has been useful for understanding the properties of lasso estimates in linear models (e.g. Park and Casella, ). In this paper, we study the lasso in the frequentist and Bayesian frameworks in the context of Cox models. For the Bayesian lasso we extend the approach by Lee et al. (). In particular, we impose the lasso penalty only on the genome features, but not on relevant clinical covariates, to allow the mandatory inclusion of important established factors. We investigate the models in high- and low-dimensional simulation settings and in an application to chronic lymphocytic leukemia.
    Type of Publication: Journal article published
    PubMed ID: 26417963
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  • 5
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    Statistics in Medicine 33 (30), 5347-5357 
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  • 6
    Keywords: APPROXIMATION ; MATRIX ; model selection ; ORACLE PROPERTIES ; VARIABLE SELECTION ; LASSO ; SINGULAR-VALUE DECOMPOSITION ; RANK ; PCA
    Abstract: MOTIVATION: Principal component analysis (PCA) is a basic tool often used in bioinformatics for visualization and dimension reduction. However, it is known that PCA may not consistently estimate the true direction of maximal variability in high-dimensional, low sample size settings, which are typical for molecular data. Assuming that the underlying signal is sparse, i.e. that only a fraction of features contribute to a principal component (PC), this estimation consistency can be retained. Most existing sparse PCA methods use L1-penalization, i.e. the lasso, to perform feature selection. But, the lasso is known to lack variable selection consistency in high dimensions and therefore a subsequent interpretation of selected features can give misleading results. RESULTS: We present S4VDPCA, a sparse PCA method that incorporates a subsampling approach, namely stability selection. S4VDPCA can consistently select the truly relevant variables contributing to a sparse PC while also consistently estimate the direction of maximal variability. The performance of the S4VDPCA is assessed in a simulation study and compared to other PCA approaches, as well as to a hypothetical oracle PCA that 'knows' the truly relevant features in advance and thus finds optimal, unbiased sparse PCs. S4VDPCA is computationally efficient and performs best in simulations regarding parameter estimation consistency and feature selection consistency. Furthermore, S4VDPCA is applied to a publicly available gene expression data set of medulloblastoma brain tumors. Features contributing to the first two estimated sparse PCs represent genes significantly over-represented in pathways typically deregulated between molecular subgroups of medulloblastoma. AVAILABILITY AND IMPLEMENTATION: Software is available at https://github.com/mwsill/s4vdpca. CONTACT: m.sill@dkfz.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    Type of Publication: Journal article published
    PubMed ID: 25861969
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  • 7
    Abstract: Mutations in isocitrate dehydrogenases (IDH) 1 and 2 frequently occur in acute myeloid leukemia (AML) and result in the production of the oncometabolite D-2-hydroxyglutarate (D2HG). D2HG has been shown to promote leukemogenesis even in the absence of mutated IDH, but the prognostic significance of pretreatment serum D2HG levels in patients with IDH-mutated AML is unclear. We measured D2HG serum levels in 84 patients with IDH-mutated AML treated in the prospective, randomized multicenter AML2003 trial of the German Study Alliance Leukemia (SAL). Multivariate Cox regression showed D2HG levels to negatively impact on event-free survival (EFS) as a continuous variable in the entire IDHmut cohort (P=0.04), with no effect on overall survival (OS). In a subgroup analysis, the negative impact of D2HG on EFS was found to be restricted to patients with mutations in IDH1 (P=0.003), adjusted for age, leukocyte count, serum lactate dehydrogenase (LDH) and European LeukemiaNet (ELN) risk score. We thus conclude that pretreatment D2HG serum levels may yield prognostic information in patients with IDH1-mutated, but not in IDH2-mutated AML, possibly due to different subcellular localizations of IDH1 and IDH2.Leukemia accepted article preview online, 19 November 2015. doi:10.1038/leu.2015.317.
    Type of Publication: Journal article published
    PubMed ID: 26582645
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  • 8
    Abstract: The broad clinical spectrum of neuroblastoma ranges from spontaneous regression to rapid progression despite intensive multimodal therapy. This diversity is not fully explained by known genetic aberrations, suggesting the possibility of epigenetic involvement in pathogenesis. In pursuit of this hypothesis, we took an integrative approach to analyze the methylomes, transcriptomes, and copy number variations in 105 cases of neuroblastoma, complemented by primary tumor- and cell line-derived global histone modification analyses and epigenetic drug treatment in vitro We found that DNA methylation patterns identify divergent patient subgroups with respect to survival and clinicobiologic variables, including amplified MYCN Transcriptome integration and histone modification-based definition of enhancer elements revealed intragenic enhancer methylation as a mechanism for high-risk-associated transcriptional deregulation. Furthermore, in high-risk neuroblastomas, we obtained evidence for cooperation between PRC2 activity and DNA methylation in blocking tumor-suppressive differentiation programs. Notably, these programs could be re-activated by combination treatments, which targeted both PRC2 and DNA methylation. Overall, our results illuminate how epigenetic deregulation contributes to neuroblastoma pathogenesis, with novel implications for its diagnosis and therapy. Cancer Res; 76(18); 5523-37. (c)2016 AACR.
    Type of Publication: Journal article published
    PubMed ID: 27635046
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  • 9
    Abstract: BACKGROUND: Skeletal involvement (SI) is observed at low prevalence in patients with diffuse large B-cell lymphoma (DLBCL). Due to the rareness of this particular condition, prospective trials for these patients are scarce. METHODS: We analyzed clinical characteristics and outcome of 75 patients with DLBCL and SI in order to identify factors with prognostic impact towards progression-free survival (PFS) and overall survival (OS). RESULTS: Limited stage disease (Ann Arbor stage IE-IIE) was present in 34 patients (45%), 41 patients (55%) had advanced stage disease (Ann Arbor stage IIIE-IVE). Outcome was generally favorable for patients with DLBCL and SI with 3-year OS of 83%. The international prognostic index (IPI) was able to distinguish between different risk groups within this specific entity. Additionally, hypercalcemia showed to be a factor significantly associated with inferior survival. In regard to first-line treatment modalities, consolidative radiotherapy was positively associated with prolonged PFS and OS while intensification of chemotherapy had no significant impact. CONCLUSIONS: In our cohort of patients with DLBCL and SI, high-risk IPI as well as presence of hypercalcemia were associated with inferior outcome. Consolidative radiotherapy had a positive impact on survival.
    Type of Publication: Journal article published
    PubMed ID: 28193188
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  • 10
    Abstract: We consider modeling competing risks data in high dimensions using a penalized cause-specific hazards (CSHs) approach. CSHs have conceptual advantages that are useful for analyzing molecular data. First, working on hazards level can further understanding of the underlying biological mechanisms that drive transition hazards. Second, CSH models can be used to extend the multistate framework for high-dimensional data. The CSH approach is implemented by fitting separate proportional hazards models for each event type (iCS). In the high-dimensional setting, this might seem too complex and possibly prone to overfitting. Therefore, we consider an extension, namely "linking" the separate models by choosing penalty tuning parameters that in combination yield best prediction of the incidence of the event of interest (penCR). We investigate whether this extension is useful with respect to prediction accuracy and variable selection. The two approaches are compared to the subdistribution hazards (SDH) model, which is an established method that naturally achieves "linking" by working on incidence level, but loses interpretability of the covariate effects. Our simulation studies indicate that in many aspects, iCS is competitive to penCR and the SDH approach. There are some instances that speak in favor of linking the CSH models, for example, in the presence of opposing effects on the CSHs. We conclude that penalized CSH models are a viable solution for competing risks models in high dimensions. Linking the CSHs can be useful in some particular cases; however, simple models using separately penalized CSH are often justified.
    Type of Publication: Journal article published
    PubMed ID: 28762523
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