This research trial studies metabolomic and other molecular profiling to identify predictive biomarkers for radiation toxicity and survival in patients with lung or brain cancers receiving radiation therapy. Studying samples of blood, urine, and tissue from patients with lung or brain cancer in the laboratory may help doctors identify and learn more about biomarkers related to cancer and predict which patients are at higher risk for developing radiation side effects and how well patients will respond to radiation treatment.
PRIMARY OBJECTIVES: I. Metabolomic and epigenetic urine and blood profiles of patients undergoing radiation therapy for lung cancer associated with the development of radiation pneumonitis. II. Metabolomic and epigenetic urine and blood profiles of patients undergoing radiation therapy for brain cancer associated with the development of radiation necrosis. SECONDARY OBJECTIVES: I. Metabolomic and epigenetic urine, blood, and tissue profiles of patients undergoing radiation therapy for lung and brain cancer associated with survival. OUTLINE: Collected blood, urine, and tissue samples are analyzed for biomarkers via metabolomic and epigenetic profiling using mass spectrometry, array, and sequencing-based technology.
Study Type
OBSERVATIONAL
Enrollment
273
Correlative studies
Arthur G. James Cancer Hospital and Solove Research Institute at Ohio State University Medical Center
Columbus, Ohio, United States
Potential predictive biomarkers for the development of radiation pneumonitis for lung cancer patients
For each biomarker, linear mixed model (with group and time interaction and repeated measurement for each patient over time) on the log-transformed expression level will be used to detect if it is differentially expressed after controlling for the effects of the co-variates (such as age, disease status, and treatment details).
Time frame: Baseline
Potential predictive biomarkers for the development of radiation necrosis for brain cancer patients
For each biomarker, linear mixed model (with group and time interaction and repeated measurement for each patient over time) on the log-transformed expression level will be used to detect if it is differentially expressed after controlling for the effects of the co-variates (such as age, disease status, and treatment details).
Time frame: Baseline
Biomarkers that are prognostically significant on disease free or overall survival, identified using the survival analysis
Kaplan Meier analysis and logrank test to identify biomarkers that are prognostically significant.
Time frame: Baseline
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.