Gene Expression Profiling and Genetic Markers in Glioblastoma Survival

Abstract

Despite the strikingly grave prognosis for older patients with glioblastomas, significant variability in patient outcome is experienced. To explore the potential for developing improved prognostic capabilities based on the elucidation of potential biological relationships, we did analyses of genes commonly mutated, amplified, or deleted in glioblastomas and DNA microarray gene expression data from tumors of glioblastoma patients of age >50 for whom survival is known. No prognostic significance was associated with genetic changes in epidermal growth factor receptor (amplified in 17 of 41 patients), TP53 (mutated in 11 of 41 patients), p16INK4A (deleted in 15 of 33 patients), or phosphatase and tensin homologue (mutated in 15 of 41 patients). Statistical analysis of the gene expression data in connection with survival involved exploration of regression models on small subsets of genes, based on computational search over multiple regression models with cross-validation to assess predictive validity. The analysis generated a set of regression models that, when weighted and combined according to posterior probabilities implied by the statistical analysis, identify patterns in expression of a small subset of genes that are associated with survival and have value in assessing survival risks. The dominant genes across such multiple regression models involve three key genes-SPARC (Osteonectin), Doublecortex, and Semaphorin3B-which play key roles in cellular migration processes. Additional analysis, based on statistical graphical association models constructed using similar computational analysis methods, reveals other genes which support the view that multiple mediators of tumor invasion may be important prognostic factor in glioblastomas in older patients. (Cancer Res 2005; 65(10): 4051-8)

Targeting SPARC expression decreases glioma cellular survival and invasion associated with reduced activities of FAK and ILK kinases

“…We now demonstrate the involvement of a novel signal transduction pathway involving SPARC, FAK, ILK and AKT in the control of glioma cell invasion and survival. As elevated SPARC expression is associated with decreased glioma patient survival (Rich et al, 2005), we believe that our results, demonstrating decreased invasion and increased cell death with siRNA directed against SPARC, suggest that decreasing SPARC expression may have therapeutic benefit for glioma patients. However, as SPARC has the potential to function both as a tumor promoter and tumor suppressor, decreasing SPARC expression may not be beneficial for all tumor types or stages.…”

“…As SPARC is expressed at low levels in normal adult brain (Mundlos et al, 1992), but highly expressed in gliomas, SPARC is an attractive target in brain tumor therapy. Increased SPARC expression levels have been linked to poor glioma patient survival (Rich et al, 2005), which suggests that the reduction of SPARC expression may have therapeutic benefit. Indeed, expression of antisense oligonucleotides against SPARC in melanoma cells blocked tumor formation (Ledda et al, 1997).…”

Identification of expressed genes characterizing long-term survival in malignant glioma patients

“…Several works (Sallinen et al, 2000;Khan et al, 2001;Ramaswamy et al, 2001;Rickman et al, 2001;Agrawal et al, 2002;Kim et al, 2002;Veer et al, 2002;Vijver et al, 2002;Boom et al, 2003;Godard et al, 2003;Hunter et al, 2003;Mischel et al, 2003;Nutt et al, 2003;Shai et al, 2003;Sorlie et al, 2003;Freije et al, 2004;Mischel et al, 2004;Hoelzinger et al, 2005;Liang et al, 2005;Nigro et al, 2005;Rich et al, 2005;Somasundaram et al, 2005;Wong et al, 2005) showed the usefulness of utilizing methods of analysis of multiple forms of data including both clinical and multiple genes, to achieve a more precise discrimination of outcomes for individual patients. The same logical use of multiple forms of data and methods of analysis has been applied in the present study to accurately achieve better classification and prediction of glioma patients.…”

“…Some of the genes that were critical components of patterns that were used to discriminate between longterm and short-term survivors are known to affect virulence of the malignant phenotype. Several groups have confirmed prognostic markers of glioma such as Insulin-like growth factor-binding protein 2 (IGFBP2) (Kim et al, 2002;Godard et al, 2003), vascular endothelial growrh factor (VEGF) (Godard et al, 2003), Osteonectin, Doublecortex, Semaphorin 3B (Rich et al, 2005) and brain-type fatty acid-binding protein (FABP7) (Liang et al, 2005).…”

“…Recently, developed microarray technology has permitted development of multi-organ cancer classification including gliomas (Ramaswamy et al, 2001;Rickman et al, 2001;Kim et al, 2002;Hunter et al, 2003;Mischel et al, 2004), identification of tumor subclasses (Khan et al, 2001;Mischel et al, 2003;Shai et al, 2003;Sorlie et al, 2003;Liang et al, 2005;Nigro et al, 2005;Wong et al, 2005), discovery of progression markers (Sallinen et al, 2000;Agrawal et al, 2002;van de Boom et al, 2003;Godard et al, 2003;Hoelzinger et al, 2005;Rich et al, 2005;Somasundaram et al, 2005) and prediction of disease outcomes (van't Veer et al, 2002;van de Vijver et al, 2002;Nutt et al, 2003;Freije et al, 2004). Unlike clinicopathological staging, molecular staging can predict long-term outcomes of any individual based on gene expression profile of the tumor at diagnosis.…”