Abstract 4791: Prognostic model of lower grade gliomas
Authors: Kosuke Aoki, Hiromichi Suzuki, Hideo Nakamura, Masahiro Mizoguchi, Tetsuya Abe, Satoru Miyano, Ichiro Takeuchi, Toshihiko Wakabayashi, Seishi Ogawa, Atsushi Natsume
Journal: Cancer Research (2015)

Abstract Background Lower grade gliomas (LGGs WHO grade II/III glioma) account for one third of all gliomas. Most LGGs generally show a slow progression, but some show a more aggressive clinical course, where several clinical/genetic factors, such as tumor size, presence of neurologic deficit before surgery and 1p19q LOH, have been reported to correlate patients’ survival. However, no large-scale studies prevent establishment of a reliable prognostication system. Methods Status of somatic mutations and copy number variations (CNVs) were investigated for 335 Japanese patients with LGG using whole exome/targeted sequencing and single nucleotide polymorphism-array karyotyping, respectively. Corresponding data were also publically available for 425 patients from the Cancer Genome Atlas (URL: http://cancergenome.nih.gov/). Correlation of genetic lesions and other parameters with overall survival (OS) was analyzed for combined 538 patients, which were divided into two sets, 269 training and 269 validation sets. First, the 269 patients of the training set were classified into 3 types according to the characteristic mutations and CNVs: Type 1 (mutated IDH with 1p/19q LOH), Type 2 (mutated IDH without 1p19q LOH), Type 3 (IDH wild type) patients. Using the LASSO Cox regression model, we built a classifier based on gene mutations, CNVs (over 5% of cases), sex, age, pathology, WHO grade and operation type (gross total resection or not). We validated the accuracy of this classifier in terms of prediction of OS in the independent group of 269 patients. Results Using the LASSO model, the patients in Type 1 and 3 were grouped into low- and high-risk groups, whereas Type 2 was not because no significant risk factors were extracted for Type 2 patients. Combining high-risk Type 1 and Type 2, between which OS was not significantly different, the entire training set was divided into 4 groups showing significantly different OS, low- and intermediate-risk groups and high- and very high-risk groups, respectively. In the training set, 5-year OS for low-, intermediate-, high-, and very high-risk groups was 100%, 82%, 49%, and 0%, which were 94%, 70%, 43%, and 14%, in the validation set, respectively. The performance of the new model was evaluated by receiver operator characteristic (ROC) analysis, which showed significantly higher accuracy compared to other models based solely on clinical/histological parameters, including pathology, WHO grade and Karnofsky performance status (KPS). Conclusion We established a new classifier based both on genetic and clinical parameters, which provides a reliable tool for predicting OS in LGGs patients and should be useful to guide therapy. Citation Format: Kosuke Aoki, Hiromichi Suzuki, Hideo Nakamura, Masahiro Mizoguchi, Tetsuya Abe, Satoru Miyano, Ichiro Takeuchi, Toshihiko Wakabayashi, Seishi Ogawa, Atsushi Natsume. Prognostic model of lower grade gliomas. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4791. doi:10.1158/1538-7445.AM2015-4791