Faculty Bibliography

Hypermethylation of the O(6)-methylguanine-DNA-methyltransferase (MGMT) gene has been shown to be associated with improved outcome in glioblastoma (GBM) and may be a predictive marker of sensitivity to alkylating agents. However, the predictive utility of this marker has not been rigorously tested with regard to sensitivity to other therapies, namely radiation. To address this issue, we assessed MGMT methylation status in a cohort of patients with GBM who underwent radiation treatment but did not receive chemotherapy as a component of adjuvant treatment. Formalin-fixed, paraffin-embedded tumor samples from 225 patients with newly diagnosed GBM were analyzed via methylation-specific, quantitative real-time polymerase chain reaction following bisulfite treatment on isolated DNA to assess MGMT promoter methylation status. In patients who received radiotherapy alone following resection, methylation of the MGMT promoter correlated with an improved response to radiotherapy. Unmethylated tumors were twice as likely to progress during radiation treatment. The median time interval between resection and tumor progression of unmethylated tumors was also nearly half that of methylated tumors. Promoter methylation was also found to confer improved overall survival in patients who did not receive adjuvant alkylating chemotherapy. Multivariable analysis demonstrated that methylation status was independent of age, Karnofsky performance score, and extent of resection as a predictor of time to progression and overall survival. Our data suggest that MGMT promoter methylation appears to be a predictive biomarker of radiation response. Since this biomarker has also been shown to predict response to alkylating agents, perhaps MGMT promoter methylation represents a general, favorable prognostic factor in GBM.

The inference of transcriptional networks that regulate transitions into physiological or pathological cellular states remains a central challenge in systems biology. A mesenchymal phenotype is the hallmark of tumour aggressiveness in human malignant glioma, but the regulatory programs responsible for implementing the associated molecular signature are largely unknown. Here we show that reverse-engineering and an unbiased interrogation of a glioma-specific regulatory network reveal the transcriptional module that activates expression of mesenchymal genes in malignant glioma. Two transcription factors (C/EBPbeta and STAT3) emerge as synergistic initiators and master regulators of mesenchymal transformation. Ectopic co-expression of C/EBPbeta and STAT3 reprograms neural stem cells along the aberrant mesenchymal lineage, whereas elimination of the two factors in glioma cells leads to collapse of the mesenchymal signature and reduces tumour aggressiveness. In human glioma, expression of C/EBPbeta and STAT3 correlates with mesenchymal differentiation and predicts poor clinical outcome. These results show that the activation of a small regulatory module is necessary and sufficient to initiate and maintain an aberrant phenotypic state in cancer cells.

Only a subset of patients with newly diagnosed glioblastoma (GBM) exhibit a response to standard therapy. To date, a biomarker panel with predictive power to distinguish treatment sensitive from treatment refractory GBM tumors does not exist. An analysis was performed using GBM microarray data from 4 independent data sets. An examination of the genes consistently associated with patient outcome, revealed a consensus 38-gene survival set. Worse outcome was associated with increased expression of genes associated with mesenchymal differentiation and angiogenesis. Application to formalin fixed-paraffin embedded (FFPE) samples using real-time reverse-transcriptase polymerase chain reaction assays resulted in a 9-gene subset which appeared robust in these samples. This 9-gene set was then validated in an additional independent sample set. Multivariate analysis confirmed that the 9-gene set was an independent predictor of outcome after adjusting for clinical factors and methylation of the methyl-guanine methyltransferase promoter. The 9-gene profile was also positively associated with markers of glioma stem-like cells, including CD133 and nestin. In sum, a multigene predictor of outcome in glioblastoma was identified which appears applicable to routinely processed FFPE samples. The profile has potential clinical application both for optimization of therapy in GBM and for the identification of novel therapies targeting tumors refractory to standard therapy.

The analysis of high-throughput data sets, such as microarray data, often requires that individual variables (genes, for example) be grouped into clusters of variables with highly correlated values across all samples. Gene shaving is an established method for generating such clusters, but is overly sensitive to the input data: changing just one sample can determine whether or not an entire cluster is found. This paper describes a clustering method based on the bootstrap aggregation of gene shaving clusters, which overcomes this and other problems, and applies the new method to a large gene expression microarray dataset from brain tumour samples.

High-grade gliomas (HGGs) represent a heterogenous group of tumors and account for most primary brain tumors. Despite aggressive therapies, they are invariably associated with poor patient outcome. These tumors include the anaplastic (World Health Organization [WHO] grade III) histologies of astrocytomas, oligodendrogliomas, and ependymomas and the WHO grade IV glioblastoma multiforme (GBM). The recent elucidation of the fundamental molecular alterations associated with these tumors has begun to unravel the critical events in their tumorigenesis but for the most part has done little to alter patient survival. Prognostication for patients with these tumors has relied principally on tumor grade and clinical factors (age, performance status, and so on) and has been inexact at best in identifying those with long-term survival potential. An even greater challenge has been to identify predictive biomarkers of therapy in the hope of tailoring a patient's therapy based on their tumor's molecular characteristics. This review discusses the molecular pathology of high-grade gliomas, with particular emphasis on anaplastic astrocytomas and GBMs because these represent the most common forms of malignant gliomas. It also focuses on the molecular signatures defined by large-scale gene expression profiling experiments because these studies are at the forefront in developing new biomarkers and identifying new therapeutic targets.

PURPOSE/OBJECTIVE:Institutional and cooperative group experience has demonstrated the feasibility of reirradiation for head and neck cancer. Limited data are available regarding the use of intensity-modulated radiotherapy (IMRT) for this indication. We reviewed our initial experience using IMRT for previously irradiated head and neck cancer patients. METHODS AND MATERIALS/METHODS:Records of 78 consecutive patients reirradiated with IMRT for head and neck cancer between 1999 and 2004 were reviewed; 74 cases were analyzed. Reirradiation was defined as any overlap between original and new radiation treatment volumes regardless of the time interval between initial and subsequent treatment. Severe reirradiation-related toxicity was defined as toxic events resulting in hospitalization, corrective surgery, or patient death. Longitudinal estimates of survival were calculated by Kaplan-Meier technique. RESULTS:Twenty (27%) patients underwent salvage surgical resection and 36 (49%) patients received chemotherapy. Median follow-up from reirradiation was 25 months. Median time interval between initial radiation and reirradiation was 46 months. Median reirradiation dose was 60 Gy. Median lifetime radiation dose was 116.1 Gy. The 2-year overall survival and locoregional control rates were 58% and 64%, respectively. Severe reirradiation related toxicity occurred in 15 patients (20%); one treatment-related death was observed. CONCLUSIONS:The use of IMRT for reirradiation of recurrent or second primary head and neck cancers resulted in encouraging local control and survival. Reirradiation-related morbidity was significant, but may be less severe than previously published reports using conventional techniques.

BACKGROUND:Controversy still exists regarding the long-term outcome of patients whose uninvolved lymph node stations are not prophylactically irradiated for non-small cell lung cancer (NSCLC) treated with definitive radiotherapy. To determine the frequency of elective nodal failure (ENF) and in-field failure (IFF), we examined a large cohort of patients with NSCLC staged with positron emission tomography (PET)/computed tomography (CT) and treated with 3-dimensional conformal radiotherapy (3D-CRT) that excluded uninvolved lymph node stations. METHODS:We retrospectively reviewed the records of 115 patients with non-small cell lung cancer treated at our institution with definitive radiation therapy with or without concurrent chemotherapy (CHT). All patients were treated with 3D-CRT, including nodal regions determined by CT or PET to be disease involved. Concurrent platinum-based CHT was administered for locally advanced disease. Patients were analyzed in follow-up for survival, local regional recurrence, and distant metastases (DM). RESULTS:The median follow-up time was 18 months (3 to 44 months) among all patients and 27 months (6 to 44 months) among survivors. The median overall survival, 2-year actuarial overall survival and disease-free survival were 19 months, 38%, and 28%, respectively. The majority of patients died from DM, the overall rate of which was 36%. Of the 31 patients with local regional failure, 26 (22.6%) had IFF, 5 (4.3%) had ENF and 2 (1.7%) had isolated ENF. For 88 patients with stage IIIA/B, the frequencies of IFF, any ENF, isolated ENF, and DM were 23 (26%), 3 (9%), 1 (1.1%) and 36 (40.9%), respectively. The comparable rates for the 22 patients with early stage node-negative disease (stage IA/IB) were 3 (13.6%), 1(4.5%), 0 (0%), and 5 (22.7%), respectively. CONCLUSION/CONCLUSIONS:We observed only a 4.3% recurrence of any ENF and a 1.7% recurrence of isolated ENF in patients with NSCLC treated with definitive 3D-CRT without prophylactic irradiation of uninvolved lymph node stations. Thus, distant metastasis and IFF remain the primary causes of treatment failure and cancer death in such patients, suggesting little value of ENI in this cohort.

INTRODUCTION/BACKGROUND:The lineages assumed by stem cells during hematopoiesis can be identified by the pattern of protein markers present on the surface of cells at different stages of differentiation. Specific antibodies directed at these markers have facilitated the isolation of hematopoietic stem cells by flow cytometry. DISCUSSION/CONCLUSIONS:Similarly, stem cells in solid organs also can be identified using cell surface markers. In addition, solid tumors have recently been found to contain small proportions of cells that are capable of proliferation, self-renewal, and differentiation into the various cell types seen in the bulk tumor. Of particular concern, these tumor-initiating cells (termed cancer stem cells when multipotency and self-renewal have been demonstrated) often display characteristics of treatment resistance, particularly to ionizing radiation. Thus, it is important to be able to identify these cells in order to better understand the mechanisms of resistance, and to be able to predict outcome and response to treatment. This depends, of course, on identifying markers that can be used to identify the cells, and for some solid tumors, a specific pattern of cell surface markers is emerging. In breast cancer, for example, the tumor-initiating cells have a characteristic Lin(-)CD44(+)CD24(-/lo) ESA(+) antigenic pattern. In cells derived from some high-grade gliomas, expression of CD133 on the cell surface appears to select for a population of tumor-initiating, treatment resistant cells. CONCLUSION/CONCLUSIONS:Because multiple markers, typically examined on single cells using flow cytometry, are used routinely to identify the subpopulation of tumor-initiating cells, and because the number of these cells is small, the challenge remains to detect them in clinical samples and to determine their ability to predict outcome and/or response to treatment, the hallmarks of established biomarkers.