Comprehensive landscape of subtype-specific coding and non-coding RNA transcripts in breast cancer.

Area of Science:

• Genomics
• Molecular Biology
• Cancer Research

Background:

• Molecular subtypes of breast cancer are crucial for prognosis and treatment decisions.
• Current classification relies on gene expression, but isoform-level analysis offers deeper insights.

Purpose of the Study:

• To comprehensively characterize breast cancer molecular subtypes using subtype-specific RNA isoforms.
• To identify novel coding, long non-coding RNA (lncRNA), and microRNA (miRNA) transcripts specific to each subtype.
• To evaluate the diagnostic and prognostic potential of these isoforms.

Main Methods:

• RNA sequencing data from 1168 The Cancer Genome Atlas (TCGA) breast cancer samples were analyzed.
• A novel quasi-Poisson model-based algorithm was employed to identify subtype-specific isoforms across the transcriptome.
• Isoform expression levels were correlated with intrinsic breast cancer subtypes.

Main Results:

• 5451 subtype-specific isoforms were discovered, with 27% showing superior classification accuracy over their corresponding genes.
• Three subtype-specific miRNAs and 707 subtype-specific lncRNAs were identified.
• lncRNA isoforms demonstrated high performance in distinguishing Luminal A and Luminal B subtypes (AUC 0.97 discovery, 0.90 validation).
• 1500 isoforms were found to be preferentially co-expressed in specific subtype pairs, including Normal-like and Basal subtypes.
• Protein-level analysis validated isoform-level findings, identifying four subtype-specific and two subtype co-expressed proteins.

Conclusions:

• Subtype-specific RNA isoforms provide a more precise molecular classification of breast cancer.
• lncRNAs and miRNAs represent valuable biomarkers for distinguishing breast cancer subtypes.
• Isoform-level analysis significantly enhances the understanding of breast cancer heterogeneity and offers potential for improved clinical decision-making.