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最优鉴别子网标志物预测化疗反应。

Optimally discriminative subnetwork markers predict response to chemotherapy.

机构信息

School of Computing Science, Simon Fraser University.

出版信息

Bioinformatics. 2011 Jul 1;27(13):i205-13. doi: 10.1093/bioinformatics/btr245.

DOI:10.1093/bioinformatics/btr245
PMID:21685072
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3117373/
Abstract

MOTIVATION

Molecular profiles of tumour samples have been widely and successfully used for classification problems. A number of algorithms have been proposed to predict classes of tumor samples based on expression profiles with relatively high performance. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein-protein interaction (PPI) data with gene expression profiles for the development of subnetwork markers in classification problems.

RESULTS

We describe a novel network-based classification algorithm (OptDis) using color coding technique to identify optimally discriminative subnetwork markers. Focusing on PPI networks, we apply our algorithm to drug response studies: we evaluate our algorithm using published cohorts of breast cancer patients treated with combination chemotherapy. We show that our OptDis method improves over previously published subnetwork methods and provides better and more stable performance compared with other subnetwork and single gene methods. We also show that our subnetwork method produces predictive markers that are more reproducible across independent cohorts and offer valuable insight into biological processes underlying response to therapy.

AVAILABILITY

The implementation is available at: http://www.cs.sfu.ca/~pdao/personal/OptDis.html

CONTACT

cenk@cs.sfu.ca; alapuk@prostatecentre.com; ccollins@prostatecentre.com.

摘要

动机

肿瘤样本的分子谱已被广泛且成功地用于分类问题。已经提出了许多算法来基于表达谱预测肿瘤样本的类别，这些算法具有相对较高的性能。然而，预测癌症治疗的反应已被证明更具挑战性，仍需要具有更高通用性的新方法。最近的研究清楚地表明，将蛋白质-蛋白质相互作用（PPI）数据与基因表达谱集成用于分类问题中的子网络标记的开发具有优势。

结果

我们描述了一种新的基于网络的分类算法（OptDis），该算法使用颜色编码技术来识别最佳区分的子网标记。我们专注于 PPI 网络，将我们的算法应用于药物反应研究：我们使用已发表的乳腺癌患者联合化疗治疗队列来评估我们的算法。我们表明，我们的 OptDis 方法优于先前发表的子网方法，并提供了比其他子网和单基因方法更好和更稳定的性能。我们还表明，我们的子网方法产生的预测标记在独立队列中更具可重复性，并为治疗反应的生物学过程提供了有价值的见解。

可用性

该实现可在以下网址获得：http://www.cs.sfu.ca/~pdao/personal/OptDis.html

联系方式

cenk@cs.sfu.ca; alapuk@prostatecentre.com; ccollins@prostatecentre.com.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/6a1b753093b2/btr245f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/f9a03d186f2a/btr245f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/85b632ca4705/btr245f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/158d096ed8a4/btr245f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/53201a9ee714/btr245f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/6a1b753093b2/btr245f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/f9a03d186f2a/btr245f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/85b632ca4705/btr245f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/158d096ed8a4/btr245f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/53201a9ee714/btr245f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e16/3117373/6a1b753093b2/btr245f5.jpg

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