Robust stratification of breast cancer subtypes using differential patterns of transcript isoform expression

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Breast cancer is a heterogeneous disease with subtypes impacting prognosis and therapy.
• Distinct gene expression profiles exist among breast cancer subtypes.
• Differential expression of transcript isoforms across subtypes remains underexplored.

Purpose of the Study:

• To investigate whether specific transcript isoforms differentiate breast cancer subtypes.
• To determine if isoform expression provides higher fidelity than standard mRNA profiles for subtype classification.
• To identify RNA processing factors involved in subtype-specific isoform regulation.

Main Methods:

• Analyzed RNA-sequencing data from Estrogen Receptor positive (ER+) and triple-negative (TN) breast cancer tumors.
• Compared isoform expression patterns with standard mRNA expression profiles for subtype differentiation.
• Validated findings in independent tumor cohorts and analyzed TCGA data.
• Investigated the role of differentially expressed RNA processing factors using RNAi knockdown experiments.

Main Results:

• Identified specific transcript isoforms that distinguish ER+ and TN breast cancer subtypes with high fidelity.
• Confirmed differential regulation of alternate promoter usage, alternative splicing, and alternate 3'UTR usage in subtypes.
• Validated the ability of isoform expression to differentiate subtypes across multiple independent cohorts.
• Discovered that RNA processing factors (YBX1, YBX2, MAGOH, MAGOHB, PCBP2) are differentially expressed and influence isoform usage.

Conclusions:

• Transcript isoform expression is a robust marker for differentiating breast cancer subtypes.
• Dysregulation of splicing is subtype-specific, reproducible, and driven by specific RNA processing factors.
• Targeting these RNA processing factors may offer novel therapeutic strategies for breast cancer.