Shilpi Arunima, Kandpal Manoj, Ji Yanrong, Seagle Brandon L, Shahabi Shohreh, Davuluri Ramana V
Northwestern University, Chicago, IL.
JCO Clin Cancer Inform. 2019 Apr;3:1-9. doi: 10.1200/CCI.18.00096.
Molecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression-based classification system for molecular subtyping of patients with high-grade serous ovarian carcinoma (HGSOC).
Unprocessed exon array (569 tumor and nine normal) and RNA sequencing (RNA-seq; 376 tumor) HGSOC data sets, with clinical annotations, were downloaded from the Genomic Data Commons portal. Sample clustering was performed by non-negative matrix factorization by using isoform-level expression estimates. The association between the subtypes and overall survival was evaluated by Cox proportional hazards regression model after adjusting for the covariates. A novel classification system was developed for HGSOC molecular subtyping. Robustness and generalizability of the gene signatures were validated using independent microarray and RNA-seq data sets.
Sample clustering recaptured the four known The Cancer Genome Atlas molecular subtypes but switched the subtype for 22% of the cases, which resulted in significant ( = .006) survival differences among the refined subgroups. After adjusting for covariate effects, the mesenchymal subgroup was found to be at an increased hazard for death compared with the immunoreactive subgroup. Both gene- and isoform-level signatures achieved more than 92% prediction accuracy when tested on independent samples profiled on the exon array platform. When the classifier was applied to RNA-seq data, the subtyping calls agreed with the predictions made from exon array data for 95% of the 279 samples profiled by both platforms.
Isoform-level expression analysis successfully stratifies patients with HGSOC into groups with differing prognosis and has led to the development of robust, platform-independent gene signatures for HGSOC molecular subtyping. The association of the refined The Cancer Genome Atlas HGSOC subtypes with overall survival, independent of covariates, enhances the clinical annotation of the HGSOC cohort.
分子癌症亚型分类是预测预后和开发新型精准医学方法的重要工具。我们开发了一种新型的基于基因表达的分类系统，该系统不依赖平台，用于高级别浆液性卵巢癌（HGSOC）患者的分子亚型分类。
从基因组数据共享平台下载未经处理的外显子阵列（569个肿瘤样本和9个正常样本）和RNA测序（RNA-seq；376个肿瘤样本）的HGSOC数据集，并带有临床注释。使用异构体水平的表达估计值，通过非负矩阵分解进行样本聚类。在调整协变量后，通过Cox比例风险回归模型评估亚型与总生存期之间的关联。开发了一种用于HGSOC分子亚型分类的新型分类系统。使用独立的微阵列和RNA-seq数据集验证基因特征的稳健性和通用性。
样本聚类重现了四种已知的癌症基因组图谱分子亚型，但22%的病例亚型发生了切换，这导致细化后的亚组之间存在显著的（P = 0.006）生存差异。在调整协变量效应后，发现间充质亚组与免疫反应亚组相比死亡风险增加。当在基于外显子阵列平台分析的独立样本上进行测试时，基因水平和异构体水平的特征预测准确率均超过92%。当将分类器应用于RNA-seq数据时，对于两个平台都分析的279个样本中的95%，亚型分类结果与从外显子阵列数据做出的预测一致。
异构体水平的表达分析成功地将HGSOC患者分层为具有不同预后的组，并导致开发出用于HGSOC分子亚型分类的稳健、不依赖平台的基因特征。细化后的癌症基因组图谱HGSOC亚型与总生存期的关联，独立于协变量，增强了HGSOC队列的临床注释。
