Significance analysis of prognostic signatures.
Significance analysis of prognostic signatures.
复制标题
DOI：
10.1371/journal.pcbi.1002875
复制
发表时间：
2013
影响因子：
4.3
通讯作者：
Haibe-Kains B
中科院分区：
文献类型：
--
作者：
Beck AH;Knoblauch NW;Hefti MM;Kaplan J;Schnitt SJ;Culhane AC;Schroeder MS;Risch T;Quackenbush J;Haibe-Kains B
转化癌症研究的一个主要目标是确定驱动癌症进展和转移的生物特征。基因组学研究中应用的一种常见技术是使用来自候选预后基因集的基因表达数据对患者进行聚类，如果所得聚类显示出统计上显着的结果分层，则将基因集与预后相关联，表明其生物学和临床重要性。最近的研究通过在几个乳腺癌数据集中显示“随机”基因集倾向于将患者分为预后可变的亚组来质疑这种方法的有效性。这项工作表明需要新的严格的统计方法来识别具有生物学信息的预后基因集。为了解决这个问题，我们开发了预后特征的显着性分析（SAPS），它将标准预后测试与基于随机基因集将患者分层为预后亚型的新预后显着性测试相结合。 SAPS 确保显着的基因集不仅能够将患者分层为预后可变组，而且还丰富了与患者预后显示出强烈单变量关联的基因，并且表现明显优于随机基因集。我们使用 SAPS 对乳腺癌和卵巢癌及其分子亚型的预后途径进行大型荟萃分析（迄今为止完成的最大的一次）。我们的分析表明，只有一小部分使用标准测量发现具有统计显着性的基因集通过 SAPS 达到了显着性。我们确定了乳腺癌和卵巢癌及其相应分子亚型的新预后特征，并且我们发现 ER 阴性乳腺癌的预后特征与卵巢癌的预后特征比 ER 阳性乳腺癌的预后特征更相似。 SAPS 是一种强大的新方法，可从临床注释的基因组数据集中导出稳健的预后生物学特征。生物医学研究的一个主要目标是识别与患者生存相关的基因组（或“生物特征”），因为这些基因可以帮助诊断和治疗疾病。使用预后关联来识别生物学信息特征的一个主要挑战是，在某些疾病中，“随机”基因集与预后相关。为了解决这个问题，我们开发了一种称为“预后特征显着性分析”（或“SAPS”）的新方法，用于识别与患者生存相关的生物学信息基因集。为了测试 SAPS 的有效性，我们使用 SAPS 对大型乳腺癌和卵巢癌元数据集中的预后特征进行亚型特异性荟萃分析。此次分析是迄今为止同类分析中规模最大的一次。我们的分析表明，只有一小部分使用标准测量发现具有统计显着性的基因集通过 SAPS 达到了显着性。我们确定了乳腺癌和卵巢癌及其相应分子亚型的新预后特征，并且证明了 ER 阴性乳腺癌和卵巢癌的预后途径之间惊人的相似性，这为这些侵袭性恶性肿瘤提出了新的共同治疗靶点。 SAPS 是一种强大的新方法，可从临床注释的基因组数据集中导出稳健的预后生物学途径。
A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that “random” gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets. A major goal in biomedical research is to identify sets of genes (or “biological signatures”) associated with patient survival, as these genes could be targeted to aid in diagnosing and treating disease. A major challenge in using prognostic associations to identify biologically informative signatures is that in some diseases, “random” gene sets are associated with prognosis. To address this problem, we developed a new method called “Significance Analysis of Prognostic Signatures” (or “SAPS”) for the identification of biologically informative gene sets associated with patient survival. To test the effectiveness of SAPS, we use SAPS to perform a subtype-specific meta-analysis of prognostic signatures in large breast and ovarian cancer meta-data sets. This analysis represents the largest of its kind ever performed. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we demonstrate a striking similarity between prognostic pathways in ER negative breast cancer and ovarian cancer, suggesting new shared therapeutic targets for these aggressive malignancies. SAPS is a powerful new method for deriving robust prognostic biological pathways from clinically annotated genomic datasets.
登录
查看更多内容
影响因子：
3.8
作者：
Sabatier, Renaud;Finetti, Pascal;Bertucci, Francois
通讯作者：
Bertucci, Francois
影响因子：
3
作者：
Alibés A;Yankilevich P;Cañada A;Díaz-Uriarte R
通讯作者：
Díaz-Uriarte R
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
45.3
作者：
Silver, Daniel P.;Richardson, Andrea L.;Garber, Judy E.
通讯作者：
Garber, Judy E.
影响因子：
5.8
作者：
Schroeder, Markus S.;Culhane, Aedin C.;Haibe-Kains, Benjamin
通讯作者：
Haibe-Kains, Benjamin