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基于网络的乳腺癌芳香化酶抑制剂反应预测。

Network-guided prediction of aromatase inhibitor response in breast cancer.

机构信息

Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Women's Cancer Research Center, Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, Magee Womens Research Institute, Pittsburgh, Pennsylvania, United States of America.

出版信息

PLoS Comput Biol. 2019 Feb 11;15(2):e1006730. doi: 10.1371/journal.pcbi.1006730. eCollection 2019 Feb.

DOI:10.1371/journal.pcbi.1006730
PMID:30742607
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6386390/
Abstract

Prediction of response to specific cancer treatments is complicated by significant heterogeneity between tumors in terms of mutational profiles, gene expression, and clinical measures. Here we focus on the response of Estrogen Receptor (ER)+ post-menopausal breast cancer tumors to aromatase inhibitors (AI). We use a network smoothing algorithm to learn novel features that integrate several types of high throughput data and new cell line experiments. These features greatly improve the ability to predict response to AI when compared to prior methods. For a subset of the patients, for which we obtained more detailed clinical information, we can further predict response to a specific AI drug.

摘要

由于肿瘤之间在突变谱、基因表达和临床指标方面存在显著异质性，因此对特定癌症治疗的反应预测变得复杂。在这里，我们专注于绝经后雌激素受体（ER）+乳腺癌肿瘤对芳香化酶抑制剂（AI）的反应。我们使用网络平滑算法来学习新的特征，这些特征整合了几种高通量数据和新的细胞系实验。与以前的方法相比，这些特征极大地提高了预测对 AI 反应的能力。对于其中一部分患者，我们获得了更详细的临床信息，我们可以进一步预测对特定 AI 药物的反应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/cf20a9515239/pcbi.1006730.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/921c18ba0a0f/pcbi.1006730.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/599137d3e0ce/pcbi.1006730.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/17c434c015d7/pcbi.1006730.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/33083a069efa/pcbi.1006730.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/cf20a9515239/pcbi.1006730.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/921c18ba0a0f/pcbi.1006730.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/599137d3e0ce/pcbi.1006730.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/17c434c015d7/pcbi.1006730.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/33083a069efa/pcbi.1006730.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9c8/6386390/cf20a9515239/pcbi.1006730.g005.jpg

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