Expression of Foxp3 in colorectal cancer but not in Treg cells correlates with disease progression in patients with colorectal cancer

Background: Regulatory T cells (Treg) expressing the transcription factor forkhead-box protein P3 (Foxp3) have been identified to counteract anti-tumor immune responses during tumor progression. Besides, Foxp3 presentation by cancer cells itself may also allow them to evade from effector T-cell responses, resulting in a survival benefit of the tumor. For colorectal cancer (CRC) the clinical relevance of Foxp3 has not been evaluated in detail. Therefore the aim of this study was to study its impact in colorectal cancer (CRC). Methods and Findings: Gene and protein analysis of tumor tissues from patients with CRC was performed to quantify the expression of Foxp3 in tumor infiltrating Treg and colon cancer cells. The results were correlated with clinicopathological parameters and patients overall survival. Serial morphological analysis demonstrated Foxp3 to be expressed in cancer cells. High Foxp3 expression of the cancer cells was associated with poor prognosis compared to patients with low Foxp3 expression. In contrast, low and high Foxp3 level in tumor infiltrating Treg cells demonstrated no significant differences in overall patient survival. Conclusions: Our findings strongly suggest that Foxp3 expression mediated by cancer cells rather than by Treg cells contribute to disease progression

Gene expression and splicing alterations analyzed by high throughput RNA sequencing of chronic lymphocytic leukemia specimens.

BackgroundTo determine differentially expressed and spliced RNA transcripts in chronic lymphocytic leukemia specimens a high throughput RNA-sequencing (HTS RNA-seq) analysis was performed.MethodsTen CLL specimens and five normal peripheral blood CD19+ B cells were analyzed by HTS RNA-seq. The library preparation was performed with Illumina TrueSeq RNA kit and analyzed by Illumina HiSeq 2000 sequencing system.ResultsAn average of 48.5 million reads for B cells, and 50.6 million reads for CLL specimens were obtained with 10396 and 10448 assembled transcripts for normal B cells and primary CLL specimens respectively. With the Cuffdiff analysis, 2091 differentially expressed genes (DEG) between B cells and CLL specimens based on FPKM (fragments per kilobase of transcript per million reads and false discovery rate, FDR q < 0.05, fold change >2) were identified. Expression of selected DEGs (n = 32) with up regulated and down regulated expression in CLL from RNA-seq data were also analyzed by qRT-PCR in a test cohort of CLL specimens. Even though there was a variation in fold expression of DEG genes between RNA-seq and qRT-PCR; more than 90 % of analyzed genes were validated by qRT-PCR analysis. Analysis of RNA-seq data for splicing alterations in CLL and B cells was performed by Multivariate Analysis of Transcript Splicing (MATS analysis). Skipped exon was the most frequent splicing alteration in CLL specimens with 128 significant events (P-value <0.05, minimum inclusion level difference >0.1).ConclusionThe RNA-seq analysis of CLL specimens identifies novel DEG and alternatively spliced genes that are potential prognostic markers and therapeutic targets. High level of validation by qRT-PCR for a number of DEG genes supports the accuracy of this analysis. Global comparison of transcriptomes of B cells, IGVH non-mutated CLL (U-CLL) and mutated CLL specimens (M-CLL) with multidimensional scaling analysis was able to segregate CLL and B cell transcriptomes but the M-CLL and U-CLL transcriptomes were indistinguishable. The analysis of HTS RNA-seq data to identify alternative splicing events and other genetic abnormalities specific to CLL is an added advantage of RNA-seq that is not feasible with other genome wide analysis

The Translation Factor eIF6 Is a Notch-Dependent Regulator of Cell Migration and Invasion

A growing body of evidence indicates that protein factors controlling translation play an important role in tumorigenesis. The protein known as eIF6 is a ribosome anti-association factor that has been implicated in translational initiation and in ribosome synthesis. Over-expression of eIF6 is observed in many natural tumours, and causes developmental and differentiation defects in certain animal models. Here we show that the transcription of the gene encoding eIF6 is modulated by the receptor Notch-1, a protein involved in embryonic development and cell differentiation, as well as in many neoplasms. Inhibition of Notch-1 signalling by γ-secretase inhibitors slowed down cell-cycle progression and reduced the amount of eIF6 in lymphoblastoid and ovarian cancer cell lines. Cultured ovarian cancer cell lines engineered to stably over-expressing eIF6 did not show significant changes in proliferation rate, but displayed an enhanced motility and invasive capacity. Inhibition of Notch-1 signalling in the cells over-expressing eIF6 was effective in slowing down the cell cycle, but did not reduce cell migration and invasion. On the whole, the results suggest that eIF6 is one of the downstream effectors of Notch-1 in the pathway that controls cell motility and invasiveness

Prognostic impact of C-REL expression in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) with a germinal center B-cell (GCB) phenotype is believed to confer a better prognosis than DLBCL with an activated B-cell (ABC) phenotype. Previous studies have suggested that nuclear factor-κB (NF-κB) activation plays an important role in the ABC subtype of DLBCL, whereas c-REL amplification is associated with the GCB subtype. Using immunohistochemical techniques, we examined 68 newly diagnosed de novo DLBCL cases (median follow-up 44 months, range 1 to 142 months) for the expression of c-REL, BCL-6, CD10, and MUM1/IRF4. Forty-four (65%) cases demonstrated positive c-REL nuclear expression. In this cohort of patients, the GCB phenotype was associated with a better overall survival (OS) than the non-GCB phenotype (Kaplan–Meier survival (KMS) analysis, p = 0.016, Breslow–Gehan–Wilcoxon test). In general, c-REL nuclear expression did not correlate with GCB vs. non-GCB phenotype, International Prognostic Index score, or OS. However, cases with a GCB phenotype and negative nuclear c-REL demonstrated better OS than cases with a GCB phenotype and positive nuclear c-REL (KMS analysis, p = 0.045, Breslow–Gehan–Wilcoxon test), whereas in cases with non-GCB phenotype, the expression of c-REL did not significantly impact the prognosis. These results suggest that c-REL nuclear expression may be a prognostic factor in DLBCL and it may improve patient risk stratification in combination with GCB/non-GCB phenotyping

From glycosylation disorders to dolichol biosynthesis defects: a new class of metabolic diseases

Polyisoprenoid alcohols are membrane lipids that are present in every cell, conserved from archaea to higher eukaryotes. The most common form, alpha-saturated polyprenol or dolichol is present in all tissues and most organelle membranes of eukaryotic cells. Dolichol has a well defined role as a lipid carrier for the glycan precursor in the early stages of N-linked protein glycosylation, which is assembled in the endoplasmic reticulum of all eukaryotic cells. Other glycosylation processes including C- and O-mannosylation, GPI-anchor biosynthesis and O-glucosylation also depend on dolichol biosynthesis via the availability of dolichol-P-mannose and dolichol-P-glucose in the ER. The ubiquity of dolichol in cellular compartments that are not involved in glycosylation raises the possibility of additional functions independent of these protein post-translational modifications. The molecular basis of several steps involved in the synthesis and the recycling of dolichol and its derivatives is still unknown, which hampers further research into this direction. In this review, we summarize the current knowledge on structural and functional aspects of dolichol metabolites. We will describe the metabolic disorders with a defect in known steps of dolichol biosynthesis and recycling in human and discuss their pathogenic mechanisms. Exploration of the developmental, cellular and biochemical defects associated with these disorders will provide a better understanding of the functions of this lipid class in human

Kernel based methods for accelerated failure time model with ultra-high dimensional data

<p>Abstract</p> <p>Background</p> <p>Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with <it>L</it>₁and <it>L_p</it>penalty have been extensively studied in survival analysis with high-dimensional genomic data. However, when the sample size <it>n </it>≪ <it>m </it>(the number of genes), directly identifying a small subset of genes from ultra-high (<it>m </it>> 10, 000) dimensional data is time-consuming and not computationally efficient. In current microarray analysis, what people really do is select a couple of thousands (or hundreds) of genes using univariate analysis or statistical tests, and then apply the LASSO-type penalty to further reduce the number of disease associated genes. This two-step procedure may introduce bias and inaccuracy and lead us to miss biologically important genes.</p> <p>Results</p> <p>The accelerated failure time (AFT) model is a linear regression model and a useful alternative to the Cox model for survival analysis. In this paper, we propose a nonlinear kernel based AFT model and an efficient variable selection method with adaptive kernel ridge regression. Our proposed variable selection method is based on the kernel matrix and dual problem with a much smaller <it>n </it>× <it>n </it>matrix. It is very efficient when the number of unknown variables (genes) is much larger than the number of samples. Moreover, the primal variables are explicitly updated and the sparsity in the solution is exploited.</p> <p>Conclusions</p> <p>Our proposed methods can simultaneously identify survival associated prognostic factors and predict survival outcomes with ultra-high dimensional genomic data. We have demonstrated the performance of our methods with both simulation and real data. The proposed method performs superbly with limited computational studies.</p

Aberrant immunoglobulin class switch recombination and switch translocations in activated B cell–like diffuse large B cell lymphoma

To elucidate the mechanisms underlying chromosomal translocations in diffuse large B cell lymphoma (DLBCL), we investigated the nature and extent of immunoglobulin class switch recombination (CSR) in these tumors. We used Southern blotting to detect legitimate and illegitimate CSR events in tumor samples of the activated B cell–like (ABC), germinal center B cell–like (GCB), and primary mediastinal B cell lymphoma (PMBL) subgroups of DLBCL. The frequency of legitimate CSR was lower in ABC DLBCL than in GCB DLBCL and PMBL. In contrast, ABC DLBCL had a higher frequency of internal deletions within the switch μ (Sμ) region compared with GCB DLBCL and PMBL. ABC DLBCLs also had frequent deletions within Sγ and other illegitimate switch recombinations. Sequence analysis revealed ongoing Sμ deletions within ABC DLBCL tumor clones, which were accompanied by ongoing duplications and activation-induced cytidine deaminase–dependent somatic mutations. Unexpectedly, short fragments derived from multiple chromosomes were interspersed within Sμ in one case. These findings suggest that ABC DLBCLs have abnormalities in the regulation of CSR that could predispose to chromosomal translocations. Accordingly, aberrant switch recombination was responsible for translocations in ABC DLBCLs involving BCL6, MYC, and a novel translocation partner, SPIB

Ki-67 as a prognostic marker in mantle cell lymphoma—consensus guidelines of the pathology panel of the European MCL Network

Mantle cell lymphoma (MCL) has a heterogeneous clinical course and is mainly an aggressive B cell non-Hodgkin lymphoma; however, there are some indolent cases The Ki-67 index, defined by the percentage of Ki-67-positive lymphoma cells on histopathological slides, has been shown to be a very powerful prognostic biomarker. The pathology panel of the European MCL Network evaluated methods to assess the Ki-67 index including stringent counting, digital image analysis, and estimation by eyeballing. Counting of 2 × 500 lymphoma cells is the gold standard to assess the Ki-67 index since this value has been shown to predict survival in prospective randomized trials of the European MCL Network. Estimation by eyeballing and digital image analysis showed a poor concordance with the gold standard (concordance correlation coefficients [CCC] between 0.29 and 0.61 for eyeballing and CCC of 0.24 and 0.37 for two methods of digital image analysis, respectively). Counting a reduced number of lymphoma cells (2 × 100 cells) showed high interobserver agreement (CCC = 0.74). Pitfalls of the Ki-67 index are discussed and guidelines and recommendations for assessing the Ki-67 index in MCL are given