Dataset Information

Incorporation of prognostic and predictive factors into glioma clinical trials.

ABSTRACT: Treatment of brain tumors is increasingly informed by biomarkers that predict patient prognosis and response to therapy. While this progress represents a great opportunity for the field of neuro-oncology, it also presents significant challenges. Biomarkers are not straightforward to identify, and previously used clinical trial paradigms are poorly suited to the task of identifying treatments effective only in selected subsets of patients. Unless investigators adapt new tools and procedures that better account for the biological diversity of gliomas, future clinical trials run the dual risk of missing important treatment effects and exposing patients to interventions destined to prove ineffective for their tumors. In this article, we will review the progress made in the past decade with respect to biomarkers in neuro-oncology, address barriers to ongoing progress, and discuss clinical trial designs that may prove useful in moving neuro-oncology fully into the era of personalized medicine.

SUBMITTER: Johnson DR

PROVIDER: S-EPMC3869572 | biostudies-other | 2013 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Incorporation of prognostic and predictive factors into glioma clinical trials.

Johnson Derek R DR Galanis Evanthia E

Current oncology reports 20130201 1

Treatment of brain tumors is increasingly informed by biomarkers that predict patient prognosis and response to therapy. While this progress represents a great opportunity for the field of neuro-oncology, it also presents significant challenges. Biomarkers are not straightforward to identify, and previously used clinical trial paradigms are poorly suited to the task of identifying treatments effective only in selected subsets of patients. Unless investigators adapt new tools and procedures that ...[more]

PMID: 23125011

Similar Datasets

Project description:PurposeGlioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma.MethodsThe expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups.ResultsFCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P < 0.001), whereas age (HR = 1.26, 95 % CI 1.04-1.52), grade (HR = 2.75, 95 % CI 2.06-3.68), tumor recurrence (HR = 2.17, 95 % CI 1.81-2.62), IDH mutant (HR = 2.46, 95 % CI 1.97-3.01) and chemotherapeutic status (HR = 1.4, 95 % CI 1.20-1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients.ConclusionsThis study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy.

Dataset Information

Incorporation of prognostic and predictive factors into glioma clinical trials.

Publications

Incorporation of prognostic and predictive factors into glioma clinical trials.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets