Analysis of target genes induced by IL-13 cytotoxin in human glioblastoma cells

Biomarkers for glioma immunotherapy: the next generation

The term "biomarker" historically refers to a single parameter, such as the expression level of a gene or a radiographic pattern, used to indicate a broader biological state. Molecular indicators have been applied to several aspects of cancer therapy: to describe the genotypic and phenotypic state of neoplastic tissue for prognosis, to predict susceptibility to anti-proliferative agents, to validate the presence of specific drug targets, and to evaluate responsiveness to therapy. For glioblastoma (GBM), immunohistochemical and radiographic biomarkers accessible to the clinical lab have informed traditional regimens, but while immunotherapies have emerged as potentially disruptive weapons against this diffusely infiltrating, heterogeneous tumor, biomarkers with strong predictive power have not been fully established. The cancer immunotherapy field, through the recently accelerated expansion of trials, is currently leveraging this wealth of clinical and biological data to define and revise the use of biomarkers for improving prognostic accuracy, personalization of therapy, and evaluation of responses across the wide variety of tumors. Technological advancements in DNA sequencing, cytometry, and microscopy have facilitated the exploration of more integrated, high-dimensional profiling of the disease system-incorporating both immune and tumor parameters-rather than single metrics, as biomarkers for therapeutic sensitivity. Here we discuss the utility of traditional GBM biomarkers in immunotherapy and how the impending transformation of the biomarker paradigm-from single markers to integrated profiles-may offer the key to bringing predictive, personalized immunotherapy to GBM patients.

Identification and characterization of alternative exon usage linked glioblastoma multiforme survival

Background

Alternative exon usage (AEU) is an important component of gene regulation. Exon expression platforms allow the detection of associations between AEU and phenotypes such as cancer. Numerous studies have identified associations between gene expression and the brain cancer glioblastoma multiforme (GBM). The few consistent gene expression biomarkers of GBM that have been reported may be due to the limited consideration of AEU and the analytical approaches used. The objectives of this study were to develop a model that accounts for the variations in expression present between the exons within a gene and to identify AEU biomarkers of GBM survival.

Methods

The expression of exons corresponding to 25,403 genes was related to the survival of 250 individuals diagnosed with GBM in a training data set. Genes exhibiting AEU in the training data set were confirmed in an independent validation data set of 78 patients. A hierarchical mixed model that allows the consideration of covariation between exons within a gene and of the effect of the epidemiological characteristics of the patients was developed to identify associations between exon expression and patient survival. This general model describes all three possible scenarios: multi-exon genes with and without AEU, and single-exon genes.

Results

AEU associated with GBM survival was identified on 2477 genes (P-value < 5.0E-04 or FDR-adjusted P-value < 0.05). G-protein coupled receptor 98 (Gpr98) and epidermal growth factor (Egf) were among the genes exhibiting AEU with 30 and 9 exons associated with GBM survival, respectively. Pathways enriched among the AEU genes included focal adhesion, ECM-receptor interaction, ABC transporters and pathways in cancer. In addition, 24 multi-exon genes without AEU and 8 single-exon genes were associated with GBM survival (FDR-adjusted P-value < 0.05).

Conclusions

The inferred patterns of AEU were consistent with in silico AS models. The hierarchical model used offered a flexible and simple way to interpret and identify associations between survival that accommodates multi-exon genes with or without AEU and single exon genes. Our results indicate that differential expression of AEU could be used as biomarker for GBM and potentially other cancers.

Physiological insights into all-trans-retinoic acid biosynthesis

All-trans-retinoic acid (atRA) provides essential support to diverse biological systems and physiological processes. Epithelial differentiation and its relationship to cancer, and embryogenesis have typified intense areas of interest into atRA function. Recently, however, interest in atRA action in the nervous system, the immune system, energy balance and obesity has increased considerably, especially concerning postnatal function. atRA action depends on atRA biosynthesis: defects in retinoid-dependent processes increasingly relate to defects in atRA biogenesis. Considerable evidence indicates that physiological atRA biosynthesis occurs via a regulated process, consisting of a complex interaction of retinoid binding-proteins and retinoid recognizing enzymes. An accrual of biochemical, physiological and genetic data have identified specific functional outcomes for the retinol dehydrogenases, RDH1, RDH10, and DHRS9, as physiological catalysts of the first step in atRA biosynthesis, and for the retinal dehydrogenases RALDH1, RALDH2, and RALDH3, as catalysts of the second and irreversible step. Each of these enzymes associates with explicit biological processes mediated by atRA. Redundancy occurs, but seems limited. Cumulative data support a model of interactions among these enzymes with retinoid binding-proteins, with feedback regulation and/or control by atRA via modulating gene expression of multiple participants. The ratio apo-CRBP1/holo-CRBP1 participates by influencing retinol flux into and out of storage as retinyl esters, thereby modulating substrate to support atRA biosynthesis. atRA biosynthesis requires the presence of both an RDH and an RALDH: conversely, absence of one isozyme of either step does not indicate lack of atRA biosynthesis at the site. This article is part of a Special Issue entitled: Retinoid and Lipid Metabolism.

Immunotherapy of malignant brain tumors

Despite aggressive multi-modality therapy including surgery, radiation, and chemotherapy, the prognosis for patients with malignant primary brain tumors remains very poor. Moreover, the non-specific nature of conventional therapy for brain tumors often results in incapacitating damage to surrounding normal brain and systemic tissues. Thus, there is an urgent need for the development of therapeutic strategies that precisely target tumor cells while minimizing collateral damage to neighboring eloquent cerebral cortex. The rationale for using the immune system to target brain tumors is based on the premise that the inherent specificity of immunologic reactivity could meet the clear need for more specific and precise therapy. The success of this modality is dependent on our ability to understand the mechanisms of immune regulation within the central nervous system (CNS), as well as counter the broad defects in host cell-mediated immunity that malignant gliomas are known to elicit. Recent advances in our understanding of tumor-induced and host-mediated immunosuppressive mechanisms, the development of effective strategies to combat these suppressive effects, and a better understanding of how to deliver immunologic effector molecules more efficiently to CNS tumors have all facilitated significant progress toward the realization of true clinical benefit from immunotherapeutic treatment of malignant gliomas.

Non-P450 aldehyde oxidizing enzymes: the aldehyde dehydrogenase superfamily

BACKGROUND

Aldehydes are highly reactive molecules. While several non-P450 enzyme systems participate in their metabolism, one of the most important is the aldehyde dehydrogenase (ALDH) superfamily, composed of NAD(P)+-dependent enzymes that catalyze aldehyde oxidation.

OBJECTIVE

This article presents a review of what is currently known about each member of the human ALDH superfamily including the pathophysiological significance of these enzymes.

METHODS

Relevant literature involving all members of the human ALDH family was extensively reviewed, with the primary focus on recent and novel findings.

CONCLUSION

To date, 19 ALDH genes have been identified in the human genome and mutations in these genes and subsequent inborn errors in aldehyde metabolism are the molecular basis of several diseases, including Sjögren-Larsson syndrome, type II hyperprolinemia, gamma-hydroxybutyric aciduria and pyridoxine-dependent seizures. ALDH enzymes also play important roles in embryogenesis and development, neurotransmission, oxidative stress and cancer. Finally, ALDH enzymes display multiple catalytic and non-catalytic functions including ester hydrolysis, antioxidant properties, xenobiotic bioactivation and UV light absorption.