Analysis of breast cancer subtypes by AP-ISA biclustering

Abstract

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1926-z) contains supplementary material, which is available to authorized users.

Model-based feature selection and clustering of RNA-seq data for unsupervised subtype discovery

“…Such pathways included signaling pathways like EIF2 and AHR, as well as pathways involved in mitosis and other molecular mechanisms of cancer. Both sets of genes also contained many gene ontology (GO) biological processes pertaining to the cell cycle, consistent with previous studies that found such enrichment in basal-like subtypes of breast cancer (Yang et al, 2017;Yang, Gao and Luo, 2019). We further validated our results by testing for overlaps with known gene sets via GSEA analysis (Mootha et al, 2003;Subramanian et al, 2005).…”

EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia

“…This unsupervised machine learning technique, also named co-clustering or subspace clustering, aims at discovering relevant local patterns in input data by extracting biclusters -submatrices with specific properties, e.g., with same values in certain rows or/and columns, correlated rows, or rows and columns, shift, scaled, or shift and scaled values in rows [9,24,30,36]. Since its first application to gene expression data over 20 years ago [7], biclustering and its algorithms have led to some meaningful discoveries in biology and biomedicine, including identification of biomarkers for cancer [42,43], diseases subtypes [8,22,47] or adverse drug effects [11].…”

Application of BiMax, POLS, and LCM-MBC to Find Bicluster on Interactions Protein between HIV-1 and Human

“…GO term is a place to confirm data gene and protein Santamaria, Theron, and Quintales (2008). Another work was done by Yang, Shen, Yuan, Zhang, and Wei (2017), combining Affinity Propagation (AP) clustering and Iterative Signature Algorithm (ISA) biclustering to analyze breast cancer. From the mixed results of the two methods, nine biclusters were obtained.…”