Description:
<jats:title>Abstract</jats:title>
<jats:p>Prognostic and predictive molecular diagnostics for patients with gliomas typically rely on multiple assays, requiring large amounts of tissue and high cost. DNA methylation has been utilized to identify prognostic subsets and high-throughput platforms exist that are suitable for archival tissue. Therefore, we developed a unified and integrated diagnostic pipeline that can assess multiple prognostic and predictive biomarkers using only the Illumina Infinium Methylation array. This pipeline includes two parts: data processing and diagnostic biomarkers. Data processing starts from the raw data and followed with quantitative sample, probe, and batch quality control. The diagnostic biomarkers include a glioma methylation assay that predicts radiation response (GaMA); tumor classification enriched for TCGA expression subclasses; copy number alterations including phosphatase and tensin homolog (PTEN) loss, epidermal growth factor receptor (EGFR) amplification, and chromosomes 1p/19q co-deletion; CpG island methylation phenotype (G-CIMP); isocitrate dehydrogenase (IDH) mutation, and O6-methylguanine-DNA methyltransferase (MGMT) promoter hypermethylation. WHO grade II-IV gliomas were analyzed in both publically available and institutional datasets. A signature was identified to effectively distinguished radiation resistant from radiation sensitive glioma stem-like cells (GSCs). Signature has been applied to 272 TCGA GBM samples from patients who received standard radiotherapy (RT). The survival analysis showed that the subgroup with RT-sensitive and RT-resistant have significant difference in survival time (log-rank test p-value = 0.0016). Gene expression subclasses prediction biomarker was build by using the revised TCGA gene expression subclasses as gold standard. The prediction accuracy in test data set was 83.5% in the homogeneous subgroup, 71.0% in the semi-heterogeneous subgroup, and 62.1% in the heterogeneous subgroup. The prediction accuracy decreased as tumor heterogeneity increased. Certain copy number alteration events were predicted by developing specific signatures. Revised methylation signatures were developed for IDH mutation and G-CIMP status respectively, which can identified 99% of those samples. 230 GBM samples with 450k data available were tested with MGMT methylation-specific real-time PCR for MGMT methylation status. The methylation-based MGMT prediction accuracy reached about 90%. In summary, we have developed a single, FFPE-based pipeline for unified and integrated determination of multiple biomarkers of malignant glioma.</jats:p>
<jats:p>Citation Format: Jie Yang, Qianghu Wang, Lihong Long, Ravesanker Ezhilarasan, Erik Sulman. UniD: unified and integrated diagnostic pipeline for malignant gliomas based on DNA methylation data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3348. doi:10.1158/1538-7445.AM2017-3348</jats:p>