title: Comprehensive landscape of subtype-specific coding and non-coding RNA transcripts in breast cancer.
abstract: Molecular classification of breast cancer into clinically relevant subtypes helps improve prognosis and adjuvant-treatment decisions. The aim of this study is to provide a better characterization of the molecular subtypes by providing a comprehensive landscape of subtype-specific isoforms including coding, long non-coding RNA and microRNA transcripts. Isoform-level expression of all coding and non-coding RNAs is estimated from RNA-sequence data of 1168 breast samples obtained from The Cancer Genome Atlas (TCGA) project. We then search the whole transcriptome systematically for subtype-specific isoforms using a novel algorithm based on a robust quasi-Poisson model. We discover 5451 isoforms specific to single subtypes. A total of 27% of the subtype-specific isoforms have better accuracy in classifying the intrinsic subtypes than that of their corresponding genes. We find three subtype-specific miRNA and 707 subtype-specific long non-coding RNAs. The isoforms from long non-coding RNAs also show high performance for separation between Luminal A and Luminal B subtypes with an AUC of 0.97 in the discovery set and 0.90 in the validation set. In addition, we discover 1500 isoforms preferentially co-expressed in two subtypes, including 369 isoforms co-expressed in both Normal-like and Basal subtypes, which are commonly considered to have distinct ER-receptor status. Finally, analyses at protein level reveal four subtype-specific proteins and two subtype co-expression proteins that successfully validate results from the isoform level.
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE 17177 Stockholm, Sweden.
authors.forename: Trung Nghia
authors.initials: TN
authors.lastname: Vu
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE 17177 Stockholm, Sweden.
authors.forename: Setia
authors.initials: S
authors.lastname: Pramana
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE 17177 Stockholm, Sweden.|Department of Molecular and Translational Medicine, University of Brescia, 25125 Brescia, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Calza
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE 17177 Stockholm, Sweden.
authors.forename: Chen
authors.initials: C
authors.lastname: Suo
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE 17177 Stockholm, Sweden.|Department of Statistics, Ewha Womans University, Seodaemun-gu, Seoul 120-750, South Korea.
authors.forename: Donghwan
authors.initials: D
authors.lastname: Lee
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE 17177 Stockholm, Sweden.
authors.forename: Yudi
authors.initials: Y
authors.lastname: Pawitan
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D011379:Prognosis; D040901:Proteomics; D062085:RNA, Long Noncoding / Q000235:genetics*; D012334:RNA, Neoplasm / Q000235:genetics*; D044140:Receptor, Angiotensin, Type 1 / Q000235:genetics; D059467:Transcriptome
chemical_list: C544065:AGTR1 protein, human; D062085:RNA, Long Noncoding; D012334:RNA, Neoplasm; D044140:Receptor, Angiotensin, Type 1
keywords: RNA sequencing; breast cancer; non-coding RNAs; subtype co-expression; subtype-specific isoforms