Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
PLoS One. 2011;6(11):e24709. doi: 10.1371/journal.pone.0024709. Epub 2011 Nov 3.
Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ~100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS).
METHODOLOGY/PRINCIPAL FINDINGS: We implemented a multivariate Cox Lasso model and median time-to-event prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).
CONCLUSIONS/SIGNIFICANCE: We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients.
浆液性卵巢癌（SeOvCa）是一种侵袭性疾病，在标准治疗后疗效差异较大，且往往不够理想。癌症基因组图谱（TCGA）从数百个原发性手术样本中提供了丰富的分子和遗传特征。这些特征证实，几乎所有患者都存在 TP53 突变，以及异常复杂的 DNA 拷贝数变化特征，患者之间存在较大差异。这就带来了一个共同的挑战，即需要利用所有新的可用数据集，并降低其混杂复杂性，以预测临床结果并确定与疾病相关的途径改变。因此，我们着手使用 TCGA 提供的多类型基因组特征（mRNA、DNA 甲基化、DNA 拷贝数改变和 microRNA）来识别用于预测无进展生存期（PFS）和总生存期（OS）的预后特征。
方法/主要发现：我们实施了多变量 Cox Lasso 模型和中位时间事件预测算法，并将其应用于从四种基因组数据类型集成的两个数据集。我们（1）通过交叉验证选择特征；（2）为患者风险分层生成预后指数；（3）直接预测连续的临床结果测量，即复发时间和生存时间。我们使用 Kaplan-Meier p 值、风险比（HR）和一致性概率估计（CPE）来评估预测性能，比较单独和集成的数据。数据集成产生了最佳的 PFS 特征（保留数据：p 值=0.008；HR=2.83；CPE=0.72）。
结论/意义：我们提供了一种预测工具，该工具输入原发性手术样本的基因组特征，并生成患者特定的复发时间和生存时间预测，以及预后风险预测。使用集成基因组特征可提高对结果的预测信息。通路分析为影响疾病进展的功能变化提供了潜在的见解。如果前瞻性验证这些预后特征，可能有助于解释旨在改善 SeOvCa 患者治疗效果的临床试验的治疗结果。
