Project description:The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of this study was to devise a machine learning method to stratify radiosensitivity and to investigate its association with copy number variations (CNVs) as markers of sensitivity to ionizing radiation. We used the Affymetrix CytoScan HD microarrays to for CNV estimation. Celluar radiosensitivity was measured by the clonogenic surviving fraction at 2 Gy (SF2).
Project description:With the whole genome SNPs array information, we could evaluate the copy number variation of samples so as to find out specific DNA aberrations in non-Hodgkin lymphma comparing with reactive hyperplasia patients.