Using microarray-based subtyping methods for breast cancer in the era of high-throughput RNA sequencing

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Breast cancer heterogeneity necessitates accurate subtyping for effective treatment.
• Current clinical subtyping relies on microarray data, which is being superseded by RNA sequencing (RNA-Seq).
• Existing subtyping methods are incompatible with RNA-Seq data.

Purpose of the Study:

• To develop an RNA-Seq data processing pipeline for probe-based breast cancer subtyping.
• To enable the use of RNA-Seq data with established microarray-based subtyping algorithms.
• To facilitate the clinical transition to RNA-Seq for transcriptomic analysis.

Main Methods:

• Developed an RNA-Seq processing pipeline mapping reads to probe set target sequences.
• Analyzed 66 breast cancer tumors with both microarray and RNA-Seq data.
• Validated the compatibility of RNA-Seq data with microarray-based subtyping using the pipeline.

Main Results:

• The pipeline enables direct comparison of RNA-Seq and microarray data.
• Established subtyping method CITBCMST can be applied to RNA-Seq data without modification.
• Successfully classified samples into six breast cancer subtypes (basL, lumA, lumB, lumC, mApo, normL).

Conclusions:

• The developed pipeline bridges the gap between microarray and RNA-Seq transcriptomics for breast cancer subtyping.
• This facilitates the adoption of RNA-Seq for future clinical transcriptomic analyses.
• Enables continued use of established subtyping signatures with next-generation sequencing technologies.