PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Large-scale genomic datasets with clinical information are crucial for cancer research and personalized medicine.
• Analyzing these complex datasets requires significant programming skills and specialized software.
• Current tools often necessitate a steep learning curve for biologists and researchers.

Purpose of the Study:

• To develop an integrated, user-friendly software solution for analyzing multi-omic cancer data.
• To facilitate subtype discovery and biomarker identification from genomic and clinical data.
• To make complex data analysis accessible to researchers without extensive programming expertise.

Main Methods:

• Development of PROMO (Profiler of Multi-Omic data), a stand-alone, interactive software.
• Integration of algorithms for data import, preprocessing, visualization, and clustering.
• Inclusion of methods for clinical label enrichment testing and survival analysis.

Main Results:

• PROMO enables comprehensive analysis of single or multi-omic datasets.
• The software facilitates rapid exploration and stratification of tumor samples into molecular subtypes.
• Demonstrated utility on a breast cancer cohort from The Cancer Genome Atlas (TCGA).

Conclusions:

• PROMO offers a unified solution for cancer genomic data analysis, including subtype discovery and biomarker identification.
• The software eliminates the need for coding, significantly easing data analysis for cancer biologists and biomedical researchers.
• PROMO is freely available for download, promoting wider accessibility in cancer research.