Analysis of breast cancer subtypes by AP-ISA biclustering

BMC Bioinformatics

Research Article

Junying Zhang¹  Liying Yang¹  Xiguo Yuan¹  Yunyan Shen¹  Jianhua Wei² 

[1] School of Computer Science and Technology, Xidian University, 710071, Xi’an, Shaanxi, China;State Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shaanxi Clinical Research Center for Oral Diseases, Department of Maxillofacial Surgery, School of Stomatology, The Fourth Military Medical University, 710032, Xi’an, Shaanxi, China;

关键词: Breast cancer;    Subtype;    Classification;    Biclustering;    Gene expression profiles;    Methylation;   

DOI  :  10.1186/s12859-017-1926-z

 received in 2017-04-28, accepted in 2017-11-06,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundGene expression profiling has led to the definition of breast cancer molecular subtypes: Basal-like, HER2-enriched, LuminalA, LuminalB and Normal-like. Different subtypes exhibit diverse responses to treatment. In the past years, several traditional clustering algorithms have been applied to analyze gene expression profiling. However, accurate identification of breast cancer subtypes, especially within highly variable LuminalA subtype, remains a challenge. Furthermore, the relationship between DNA methylation and expression level in different breast cancer subtypes is not clear.ResultsIn this study, a modified ISA biclustering algorithm, termed AP-ISA, was proposed to identify breast cancer subtypes. Comparing with ISA, AP-ISA provides the optimized strategy to select seeds and thresholds in the circumstance that prior knowledge is absent. Experimental results on 574 breast cancer samples were evaluated using clinical ER/PR information, PAM50 subtypes and the results of five peer to peer methods. One remarkable point in the experiment is that, AP-ISA divided the expression profiles of the luminal samples into four distinct classes. Enrichment analysis and methylation analysis showed obvious distinction among the four subgroups. Tumor variability within the Luminal subtype is observed in the experiments, which could contribute to the development of novel directed therapies.ConclusionsAiming at breast cancer subtype classification, a novel biclustering algorithm AP-ISA is proposed in this paper. AP-ISA classifies breast cancer into seven subtypes and we argue that there are four subtypes in luminal samples. Comparison with other methods validates the effectiveness of AP-ISA. New genes that would be useful for targeted treatment of breast cancer were also obtained in this study.

【 授权许可】

CC BY   
© The Author(s). 2017

【 图 表 】

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