Dimitrieva Slavica, Schlapbach Ralph, Rehrauer Hubert
Functional Genomics Center Zurich, ETH Zurich and University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.
Biol Direct. 2016 Dec 20;11(1):68. doi: 10.1186/s13062-016-0170-1.
Kidney renal clear cell carcinoma (KIRC) is a type of cancer that is resistant to chemotherapy and radiotherapy and has limited treatment possibilities. Large-scale molecular profiling of KIRC tumors offers a great potential to uncover the genetic and epigenetic changes underlying this disease and to improve the clinical management of KIRC patients. However, in practice the clinicians and researchers typically focus on single-platform molecular data or on a small set of genes. Using molecular and clinical data of over 500 patients, we have systematically studied which type of molecular data is the most informative in predicting the clinical outcome of KIRC patients, as a standalone platform and integrated with clinical data.
We applied different computational approaches to preselect on survival-predictive genomic markers and evaluated the usability of mRNA/miRNA/protein expression data, copy number variation (CNV) data and DNA methylation data in predicting survival of KIRC patients. Our analyses show that expression and methylation data have statistically significant predictive powers compared to a random guess, but do not perform better than predictions on clinical data alone. However, the integration of molecular data with clinical variables resulted in improved predictions. We present a set of survival associated genomic loci that could potentially be employed as clinically useful biomarkers.
Our study evaluates the survival prediction of different large-scale molecular data of KIRC patients and describes the prognostic relevance of such data over clinical-variable-only models. It also demonstrates the survival prognostic importance of methylation alterations in KIRC tumors and points to the potential of epigenetic modulators in KIRC treatment.
An extended abstract of this research paper was selected for the CAMDA Satellite Meeting to ISMB 2015 by the CAMDA Programme Committee. The full research paper then underwent one round of Open Peer Review under a responsible CAMDA Programme Committee member, Djork-Arné Clevert, PhD (Bayer AG, Germany). Open Peer Review was provided by Martin Otava, PhD (Janssen Pharmaceutica, Belgium) and Hendrik Luuk, PhD (The Centre for Disease Models and Biomedical Imaging, University of Tartu, Estonia). The Reviewer comments section shows the full reviews and author responses.
肾透明细胞癌（KIRC）是一种对化疗和放疗耐药且治疗选择有限的癌症。对KIRC肿瘤进行大规模分子分析，为揭示该疾病潜在的基因和表观遗传变化以及改善KIRC患者的临床管理提供了巨大潜力。然而，在实际中，临床医生和研究人员通常关注单一平台的分子数据或一小部分基因。我们利用500多名患者的分子和临床数据，系统地研究了哪种类型的分子数据在预测KIRC患者临床结局方面最具信息价值，包括作为独立平台以及与临床数据整合时。
我们应用不同的计算方法对生存预测基因组标记进行预选，并评估mRNA/miRNA/蛋白质表达数据、拷贝数变异（CNV）数据和DNA甲基化数据在预测KIRC患者生存方面的可用性。我们的分析表明，与随机猜测相比，表达和甲基化数据具有统计学上显著的预测能力，但并不比仅基于临床数据的预测表现更好。然而，分子数据与临床变量的整合导致了更好的预测。我们提出了一组与生存相关的基因组位点，这些位点有可能被用作临床上有用的生物标志物。
我们的研究评估了KIRC患者不同大规模分子数据的生存预测，并描述了此类数据相对于仅基于临床变量模型的预后相关性。它还证明了KIRC肿瘤中甲基化改变在生存预后方面的重要性，并指出了表观遗传调节剂在KIRC治疗中的潜力。
本研究论文的扩展摘要被CAMDA计划委员会选入2015年ISMB的CAMDA卫星会议。完整的研究论文随后在负责的CAMDA计划委员会成员Djork-Arné Clevert博士（德国拜耳公司）的主持下进行了一轮开放同行评审。开放同行评审由Martin Otava博士（比利时杨森制药公司）和Hendrik Luuk博士（爱沙尼亚塔尔图大学疾病模型与生物医学成像中心）提供。评审人评论部分展示了完整的评审意见和作者回复。
