Meta-Analysis Identifies NF-κB as a Therapeutic Target in Renal Cancer

Objective

To determine the expression patterns of NF-κB regulators and target genes in clear cell renal cell carcinoma (ccRCC), their correlation with von Hippel Lindau (VHL) mutational status, and their association with survival outcomes.

Methods

Meta-analyses were carried out on published ccRCC gene expression datasets by RankProd, a non-parametric statistical method. DEGs with a False Discovery Rate of < 0.05 by this method were considered significant, and intersected with a curated list of NF-κB regulators and targets to determine the nature and extent of NF-κB deregulation in ccRCC.

Results

A highly-disproportionate fraction (~40%; p < 0.001) of NF-κB regulators and target genes were found to be up-regulated in ccRCC, indicative of elevated NF-κB activity in this cancer. A subset of these genes, comprising a key NF-κB regulator (IKBKB) and established mediators of the NF-κB cell-survival and pro-inflammatory responses (MMP9, PSMB9, and SOD2), correlated with higher relative risk, poorer prognosis, and reduced overall patient survival. Surprisingly, levels of several interferon regulatory factors (IRFs) and interferon target genes were also elevated in ccRCC, indicating that an ‘interferon signature’ may represent a novel feature of this disease. Loss of VHL gene expression correlated strongly with the appearance of NF-κB- and interferon gene signatures in both familial and sporadic cases of ccRCC. As NF-κB controls expression of key interferon signaling nodes, our results suggest a causal link between VHL loss, elevated NF-κB activity, and the appearance of an interferon signature during ccRCC tumorigenesis.

Conclusions

These findings identify NF-κB and interferon signatures as clinical features of ccRCC, provide strong rationale for the incorporation of NF-κB inhibitors and/or and the exploitation of interferon signaling in the treatment of ccRCC, and supply new NF-κB targets for potential therapeutic intervention in this currently-incurable malignancy.     

A Simplified Approach for the Molecular Classification of Glioblastomas

Glioblastoma (GBM) is the most common malignant primary brain tumors in adults and exhibit striking aggressiveness. Although GBM constitute a single histological entity, they exhibit considerable variability in biological behavior, resulting in significant differences in terms of prognosis and response to treatment. In an attempt to better understand the biology of GBM, many groups have performed high-scale profiling studies based on gene or protein expression. These studies have revealed the existence of several GBM subtypes. Although there remains to be a clear consensus, two to four major subtypes have been identified. Interestingly, these different subtypes are associated with both differential prognoses and responses to therapy. In the present study, we investigated an alternative immunohistochemistry (IHC)-based approach to achieve a molecular classification for GBM. For this purpose, a cohort of 100 surgical GBM samples was retrospectively evaluated by immunohistochemical analysis of EGFR, PDGFRA and p53. The quantitative analysis of these immunostainings allowed us to identify the following two GBM subtypes: the “Classical-like” (CL) subtype, characterized by EGFR-positive and p53- and PDGFRA-negative staining and the “Proneural-like” (PNL) subtype, characterized by p53- and/or PDGFRA-positive staining. This classification represents an independent prognostic factor in terms of overall survival compared to age, extent of resection and adjuvant treatment, with a significantly longer survival associated with the PNL subtype. Moreover, these two GBM subtypes exhibited different responses to chemotherapy. The addition of temozolomide to conventional radiotherapy significantly improved the survival of patients belonging to the CL subtype, but it did not affect the survival of patients belonging to the PNL subtype. We have thus shown that it is possible to differentiate between different clinically relevant subtypes of GBM by using IHC-based profiling, a method that is advantageous in its ease of daily implementation and in large-scale clinical application.     

Identification of miRNAs Expression Profile in Gastric Cancer Using Self-Organizing Maps (SOM)

In this paper, an unsupervised artificial neural network was implemented to identify the patters of specific signatures. The network was based on the differential expression of miRNAs (under or over expression) found in healthy or cancerous gastric tissues. Among the tissues analyzes, the neural network evaluated 514 miRNAs of gastric tissue that exhibited significant differential expression. The result suggested a specific expression signature nine miRNAs (hsa-mir-21, hsa-mir-29a, hsa-mir-29c, hsa-mir-148a, hsa-mir-141, hsa-let-7b, hsa-mir-31, hsa-mir-451, and hsa-mir-192), all with significant values (p-value < 0.01 and fold change > 5) that clustered the samples into two groups: healthy tissue and gastric cancer tissue. The results obtained “in silico” must be validated in a molecular biology laboratory; if confirmed, this method may be used in the future as a risk marker for gastric cancer development.     

Stromal and epithelial networks: Temporal gene expression profiling during invasive neoplasia

The interaction between tumor cells and the stromal microenvironment is a critical factor in cancer development and progression. A recent study from the Khavari group profiled the expression changes during progression to invasion in a Ras-inducible model of human epithelial neoplasia and used network modeling to analyze the molecular interactions. Human dermis was seeded with H-Ras- and IκBα-expressing keratinocytes then grafted on to immune-deficient mice. The epithelial and stromal gene expression profiles were captured during progression from quiescent epithelial tissue to in situ neoplasia to invasive neoplasia. A subset of these altered genes was compiled into a “core tumor progression signature” (CTPS), which was shown to have clinical relevance in several cancer types. Network modeling of the CTPS revealed highly interconnected “hubs”, which was dominated by extracellular matrix-related genes, including β₁ integrin. Targeting integrin β₁ functionality reduced Ras-driven tumorigenesis in vivo and validated the network modeling strategy for predicting genes essential to neoplasia. By integrating temporal analysis of both the epithelial and stromal compartments with network modeling of molecular interactions, this work has described an effective strategy for identifying highly interconnected targets essential to tumor development.Key words: skin model, tumor invasion, microarray, network modelling, molecular interactions     

Gene Expression Profile of Glioblastoma Peritumoral Tissue: An Ex Vivo Study

The gene expression pattern of glioblastoma (GBM) is well documented but the expression profile of brain adjacent to tumor is not yet analysed. This may help to understand the oncogenic pathway of GBM development. We have established the genome-wide expression profiles of samples isolated from GBM tumor mass, white matter adjacent to tumor (apparently free of tumor cells), and white matter controls by using the Affymetrix HG-U133 arrays. Array-CGH (aCGH) was also performed to detect genomic alterations. Among genes dysregulated in peritumoral white matter, 15 were over-expressed, while 42 were down-regulated when compared to white matter controls. A similar expression profile was detected in GBM cells. Growth, proliferation and cell motility/adhesion-associated genes were up-regulated while genes involved in neurogenesis were down-regulated. Furthermore, several tumor suppressor genes along with the KLRC1 (a member of natural killer receptor) were also down-regulated in the peritumoral brain tissue. Several mosaic genomic lesions were detected by aCGH, mostly in tumor samples and several GBM-associated mosaic genomic lesions were also present in the peritumoral brain tissue, with a similar mosaicism pattern. Our data could be explained by a dilution of genes expressed from tumor cells infiltrating the peritumour tissue. Alternatively, these findings could be substained by a relevant amount of “apparently normal” cells presenting a gene profile compatible with a precancerous state or even “quiescent” cancer cells. Otherwise, the recurrent tumor may arise from both infiltrating tumor cells and from an interaction and recruitment of apparently normal cells in the peritumor tissue by infiltrating tumor cells.     

microRNA-100 Targets SMRT/NCOR2, Reduces Proliferation,and Improves Survival in Glioblastoma Animal Models

Glioblastoma (GBM) is the most frequently diagnosed malignant human glioma, and current median patient survival is less than two years despite maximal surgery followed by temozolomide chemoradiation therapies. Novel microRNA-related therapies are now being developed for cancers such as GBM. Differential microRNA expression profiling revealed that miR-100 expression is down-regulated in GBM compared to normal controls. We report that miR-100 expression reduces GBM tumorigenicity. In vitro, four GBM lines (U87, U251, 22T, and 33T) demonstrated reduced proliferation 24 hours after transient miR100 overexpression via transfection. miR-100 triggered cell death an average 70% more than scrambled miR controls 24 hours after transient transfection (p < 0.01). miR-100 targeted inhibition of the “silencing mediator of retinoid or thyroid hormone receptor-2” (SMRT/NCOR2) gene was confirmed via reporter assays. Ki67 proliferation index was decreased 40% in tumor xenografts generated from stable miR-100 transfected GBM lines versus controls (p < 0.01). Furthermore, treatment of tumor xenografts with a single pre-mir-100 injection (60 pmol) significantly extended survival of mice bearing intracranial GBM xenografts 25% more than scrambled controls (p < 0.01; n=8). These studies establish miR-100’s effect on tumor GBM growth, and suggest clinical potential for microRNA-related GBM therapy.     

Integrated Multiple “-omics” Data Reveal Subtypes of Hepatocellular Carcinoma

Hepatocellular carcinoma is one of the most heterogeneous cancers, as reflected by its multiple grades and difficulty to subtype. In this study, we integrated copy number variation, DNA methylation, mRNA, and miRNA data with the developed “cluster of cluster” method and classified 256 HCC samples from TCGA (The Cancer Genome Atlas) into five major subgroups (S1-S5). We observed that this classification was associated with specific mutations and protein expression, and we detected that each subgroup had distinct molecular signatures. The subclasses were associated not only with survival but also with clinical observations. S1 was characterized by bulk amplification on 8q24, TP53 mutation, low lipid metabolism, highly expressed onco-proteins, attenuated tumor suppressor proteins and a worse survival rate. S2 and S3 were characterized by telomere hypomethylation and a low expression of TERT and DNMT1/3B. Compared to S2, S3 was associated with less copy number variation and some good prognosis biomarkers, including CRP and CYP2E1. In contrast, the mutation rate of CTNNB1 was higher in S3. S4 was associated with bulk amplification and various molecular characteristics at different biological levels. In summary, we classified the HCC samples into five subgroups using multiple “-omics” data. Each subgroup had a distinct survival rate and molecular signature, which may provide information about the pathogenesis of subtypes in HCC.