Cutoff Finder: a comprehensive and straightforward Web application enabling rapid biomarker cutoff optimization.

Area of Science:

• Biostatistics
• Bioinformatics
• Biomarker Discovery

Background:

• Gene and protein expression data require cutoff points for clinical decisions.
• Current methods for biomarker cutoff determination lack standardization and dedicated software.
• Stratifying patients based on continuous variables necessitates reliable cutoff identification.

Purpose of the Study:

• To introduce Cutoff Finder, an online tool for optimizing and visualizing biomarker cutoff points.
• To provide a standardized method for determining clinically relevant thresholds for biomarkers.
• To facilitate the development and validation of new diagnostic biomarkers.

Main Methods:

• Development of Cutoff Finder, a web-accessible bundle of optimization and visualization algorithms.
• Implementation of methods based on marker distribution and correlation with clinical outcomes (e.g., survival).
• Application of Cutoff Finder to analyze estrogen receptor (ER) and progesterone receptor (PgR) gene expression in breast cancer.

Main Results:

• Cutoff Finder offers multiple methods for cutoff optimization, including distribution-based and outcome-correlated approaches.
• The tool was successfully applied to analyze ER and PgR expression in breast cancer.
• Analysis correlated gene expression cutoffs with immunohistological status and distant metastasis-free survival.

Conclusions:

• Cutoff Finder addresses a critical gap in biostatistical software for biomarker analysis.
• The tool enables efficient optimization and validation of diagnostic and prognostic biomarkers.
• Accessible online, Cutoff Finder is expected to accelerate the clinical translation of novel biomarkers.