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通过贝叶斯统计建模预测乳腺癌细胞中导致获得性耐药的信号串扰。

Prediction of signaling cross-talks contributing to acquired drug resistance in breast cancer cells by Bayesian statistical modeling.

作者信息

Azad A K M, Lawen Alfons, Keith Jonathan M

机构信息

School of Mathematical Science, Monash University, Wellington Road, Clayton, VIC, Australia.

Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Wellington Road, Clayton, VIC, Australia.

出版信息

BMC Syst Biol. 2015 Jan 20;9:2. doi: 10.1186/s12918-014-0135-x.

DOI:10.1186/s12918-014-0135-x
PMID:25599599
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4307189/
Abstract

BACKGROUND

Initial success of inhibitors targeting oncogenes is often followed by tumor relapse due to acquired resistance. In addition to mutations in targeted oncogenes, signaling cross-talks among pathways play a vital role in such drug inefficacy. These include activation of compensatory pathways and altered activities of key effectors in other cell survival and growth-associated pathways.

RESULTS

We propose a computational framework using Bayesian modeling to systematically characterize potential cross-talks among breast cancer signaling pathways. We employed a fully Bayesian approach known as the p 1-model to infer posterior probabilities of gene-pairs in networks derived from the gene expression datasets of ErbB2-positive breast cancer cell-lines (parental, lapatinib-sensitive cell-line SKBR3 and the lapatinib-resistant cell-line SKBR3-R, derived from SKBR3). Using this computational framework, we searched for cross-talks between EGFR/ErbB and other signaling pathways from Reactome, KEGG and WikiPathway databases that contribute to lapatinib resistance. We identified 104, 188 and 299 gene-pairs as putative drug-resistant cross-talks, respectively, each comprised of a gene in the EGFR/ErbB signaling pathway and a gene from another signaling pathway, that appear to be interacting in resistant cells but not in parental cells. In 168 of these (distinct) gene-pairs, both of the interacting partners are up-regulated in resistant conditions relative to parental conditions. These gene-pairs are prime candidates for novel cross-talks contributing to lapatinib resistance. They associate EGFR/ErbB signaling with six other signaling pathways: Notch, Wnt, GPCR, hedgehog, insulin receptor/IGF1R and TGF- β receptor signaling. We conducted a literature survey to validate these cross-talks, and found evidence supporting a role for many of them in contributing to drug resistance. We also analyzed an independent study of lapatinib resistance in the BT474 breast cancer cell-line and found the same signaling pathways making cross-talks with the EGFR/ErbB signaling pathway as in the primary dataset.

CONCLUSIONS

Our results indicate that the activation of compensatory pathways can potentially cause up-regulation of EGFR/ErbB pathway genes (counteracting the inhibiting effect of lapatinib) via signaling cross-talk. Thus, the up-regulated members of these compensatory pathways along with the members of the EGFR/ErbB signaling pathway are interesting as potential targets for designing novel anti-cancer therapeutics.

摘要

背景

靶向癌基因的抑制剂最初取得成功后,肿瘤常因获得性耐药而复发。除了靶向癌基因的突变外,信号通路间的信号串扰在这种药物失效中起着至关重要的作用。这包括补偿性通路的激活以及其他细胞存活和生长相关通路中关键效应器活性的改变。

结果

我们提出了一个使用贝叶斯建模的计算框架,以系统地表征乳腺癌信号通路之间潜在的信号串扰。我们采用了一种称为p 1模型的全贝叶斯方法,来推断源自ErbB2阳性乳腺癌细胞系(亲本细胞系、对拉帕替尼敏感的细胞系SKBR3以及源自SKBR3的对拉帕替尼耐药的细胞系SKBR3-R)基因表达数据集的网络中基因对的后验概率。使用这个计算框架,我们从Reactome、KEGG和WikiPathway数据库中搜索了EGFR/ErbB与其他信号通路之间有助于拉帕替尼耐药的信号串扰。我们分别鉴定出104、188和299个基因对作为假定的耐药信号串扰,每个基因对都由EGFR/ErbB信号通路中的一个基因和另一个信号通路中的一个基因组成,这些基因对在耐药细胞中似乎相互作用,而在亲本细胞中则不然。在这些(不同的)基因对中的168个中,两个相互作用的伙伴在耐药条件下相对于亲本条件均上调。这些基因对是导致拉帕替尼耐药的新型信号串扰的主要候选者。它们将EGFR/ErbB信号与其他六个信号通路联系起来:Notch、Wnt、GPCR、刺猬、胰岛素受体/IGF1R和TGF-β受体信号通路。我们进行了文献调查以验证这些信号串扰,并发现有证据支持其中许多在导致耐药中起作用。我们还分析了一项关于BT474乳腺癌细胞系中拉帕替尼耐药性的独立研究,发现与主要数据集中相同的信号通路与EGFR/ErbB信号通路发生信号串扰。

结论

我们的结果表明,补偿性通路的激活可能通过信号串扰导致EGFR/ErbB通路基因上调(抵消拉帕替尼的抑制作用)。因此,这些补偿性通路的上调成员以及EGFR/ErbB信号通路的成员作为设计新型抗癌疗法的潜在靶点很有意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d18a/4307189/a8e51a7c5c80/12918_2014_135_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d18a/4307189/af4aaf1d76aa/12918_2014_135_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d18a/4307189/33be2f20940e/12918_2014_135_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d18a/4307189/357bb2682505/12918_2014_135_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d18a/4307189/a8e51a7c5c80/12918_2014_135_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d18a/4307189/af4aaf1d76aa/12918_2014_135_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d18a/4307189/33be2f20940e/12918_2014_135_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d18a/4307189/357bb2682505/12918_2014_135_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d18a/4307189/a8e51a7c5c80/12918_2014_135_Fig4_HTML.jpg

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