Pathway-based classification of cancer subtypes.
Pathway-based classification of cancer subtypes.
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DOI：
10.1186/1745-6150-7-21
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发表时间：
2012-07-03
期刊：
影响因子：
5.5
通讯作者：
DeLisi C
中科院分区：
文献类型：
--
作者：
Kim S;Kon M;DeLisi C
基于基因表达谱的分子标记已经在实验和临床环境中用于区分癌症的分期、分级、生存时间、转移和药物敏感性。然而，大多数重要的基因标记在数据集中是不稳定的(不可重现的)。为了区分癌症亚型，我们介绍了一种将癌症标记物表示为两级分层特征向量的标准化方法，该特征向量具有基本基因水平和第二水平(更稳定)的途径标记。这扩展了标准基因表达阵列，使其具有直接从现成的基因集浓缩算法(如GSEA)获得的新的途径水平激活特征。这种所谓的基于途径的表达阵列在数据集上的重复性要高得多。这种重复性对于基因组标记物的临床应用将是重要的，并增强了目前被接受的癌症分类方案。目前的方法产生了更稳定的(可重复性的)基于通路的标记物，用于区分乳腺癌转移和卵巢癌生存时间。在乳腺癌转移的两个数据集中，标准显著基因生物标记物的交集总计占所选基因的7.47%，而基于途径的标记物的交集为17.65%；卵巢癌数据集的相应百分比分别为20.65%和33.33%。在卵巢长存活组和乳房无转移组中，有三条途径，包括1型糖尿病、细胞因子-细胞因子-受体相互作用和Hedgehog信号通路(均与癌症有关)，在卵巢长存活组和乳房无转移组中均有丰富的表达。此外，结合途径和基因信息，我们分别发现了5个(ID4、ANXA4、CXCL9、Mylk、FBXL7)和6个(SqLE、E2F1、Pttg1、TSTA3、BUB1B、MAD2L1)与卵巢癌和乳腺癌相关的已知癌基因。在癌症分期、分类和分析过程中标准化基因组数据分析是重要的，因为它对临床前和临床研究都有影响。使用基于途径的生物标记物作为特征的诊断和预测范例可能是基于生物标记物的癌症分析过程的重要部分，由此产生的规范(临床可重现的)生物标记物对于标准化基因组数据可能是重要的。我们期望这些典型生物标志物的识别将提高高通量数据集在诊断和预后应用中的临床实用性。本文由John McDonald(由I.King Jordon提名)、尤金·库宁、Nathan Bowen(由I.King Jordon提名)和Ekaterina Kotelnikova(由Mikhail Gelfand提名)审阅。
Molecular markers based on gene expression profiles have been used in experimental and clinical settings to distinguish cancerous tumors in stage, grade, survival time, metastasis, and drug sensitivity. However, most significant gene markers are unstable (not reproducible) among data sets. We introduce a standardized method for representing cancer markers as 2-level hierarchical feature vectors, with a basic gene level as well as a second level of (more stable) pathway markers, for the purpose of discriminating cancer subtypes. This extends standard gene expression arrays with new pathway-level activation features obtained directly from off-the-shelf gene set enrichment algorithms such as GSEA. Such so-called pathway-based expression arrays are significantly more reproducible across datasets. Such reproducibility will be important for clinical usefulness of genomic markers, and augment currently accepted cancer classification protocols. The present method produced more stable (reproducible) pathway-based markers for discriminating breast cancer metastasis and ovarian cancer survival time. Between two datasets for breast cancer metastasis, the intersection of standard significant gene biomarkers totaled 7.47% of selected genes, compared to 17.65% using pathway-based markers; the corresponding percentages for ovarian cancer datasets were 20.65% and 33.33% respectively. Three pathways, consisting of Type_1_diabetes mellitus, Cytokine-cytokine_receptor_interaction and Hedgehog_signaling (all previously implicated in cancer), are enriched in both the ovarian long survival and breast non-metastasis groups. In addition, integrating pathway and gene information, we identified five (ID4, ANXA4, CXCL9, MYLK, FBXL7) and six (SQLE, E2F1, PTTG1, TSTA3, BUB1B, MAD2L1) known cancer genes significant for ovarian and breast cancer respectively. Standardizing the analysis of genomic data in the process of cancer staging, classification and analysis is important as it has implications for both pre-clinical as well as clinical studies. The paradigm of diagnosis and prediction using pathway-based biomarkers as features can be an important part of the process of biomarker-based cancer analysis, and the resulting canonical (clinically reproducible) biomarkers can be important in standardizing genomic data. We expect that identification of such canonical biomarkers will improve clinical utility of high-throughput datasets for diagnostic and prognostic applications. This article was reviewed by John McDonald (nominated by I. King Jordon), Eugene Koonin, Nathan Bowen (nominated by I. King Jordon), and Ekaterina Kotelnikova (nominated by Mikhail Gelfand).
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