A collection of annotated and harmonized human breast cancer transcriptome datasets, including immunologic...

Area of Science:

• Bioinformatics
• Genomics
• Translational Research

Background:

• High-throughput research generates vast amounts of public data.
• Utilizing this data is crucial for biomedical advancements.
• Interactive tools are needed to facilitate data exploration.

Purpose of the Study:

• To introduce the Gene Expression Browser (GXB), an interactive web application.
• To present a curated compendium of 13 human breast cancer datasets (2142 transcriptome profiles).
• To demonstrate GXB's utility for analyzing breast cancer data with a focus on immunologic classification.

Main Methods:

• Developed an interactive data browsing and visualization web application (GXB).
• Curated and harmonized 13 public human breast cancer datasets.
• Classified samples by immune-based systems, intrinsic subtypes, and clinical parameters.
• Integrated classification data into datasets and uploaded to GXB.
• Generated ranked gene lists based on group comparisons.

Main Results:

• Uploaded annotated and harmonized datasets to GXB.
• Categorized samples based on immunologic tumor response, molecular subtypes, and clinical parameters.
• Demonstrated GXB's capability to evaluate gene expression, identify differential expression, and explore variable associations.

Conclusions:

• GXB provides an interactive platform for exploring complex breast cancer datasets.
• The resource aids in understanding gene expression in relation to immunologic classification.
• Facilitates deeper insights into breast cancer biology and potential therapeutic targets.